WO2024128090A1 - Dispositif de traitement d'informations, système de commande, procédé de recherche et programme de recherche - Google Patents

Dispositif de traitement d'informations, système de commande, procédé de recherche et programme de recherche Download PDF

Info

Publication number
WO2024128090A1
WO2024128090A1 PCT/JP2023/043618 JP2023043618W WO2024128090A1 WO 2024128090 A1 WO2024128090 A1 WO 2024128090A1 JP 2023043618 W JP2023043618 W JP 2023043618W WO 2024128090 A1 WO2024128090 A1 WO 2024128090A1
Authority
WO
WIPO (PCT)
Prior art keywords
control
inference
information processing
unit
processing device
Prior art date
Application number
PCT/JP2023/043618
Other languages
English (en)
Japanese (ja)
Inventor
翔太 林
俊平 野原
裕司 白石
Original Assignee
日立造船株式会社
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 日立造船株式会社 filed Critical 日立造船株式会社
Publication of WO2024128090A1 publication Critical patent/WO2024128090A1/fr

Links

Images

Definitions

  • the present invention relates to setting parameters in a control system that controls a specified control object according to the results of inference using an inference model.
  • Patent Document 1 describes a waste incineration plant that incinerates waste and generates electricity using the heat generated during incineration, where a machine-learned neural network model is used to predict the amount of steam generated after a specified time, and operation is controlled based on the prediction results.
  • This type of problem is not limited to control systems used in plants, but is a common problem that arises in control systems that control any control object according to the results of inference using an arbitrary inference model. Furthermore, this type of problem is not limited to determining the content of operational control, but is a common problem that arises when setting various parameters related to inference and control.
  • One aspect of the present invention aims to provide an information processing device or the like that enables appropriate setting of parameters related to inference and control, regardless of the skills of individuals, in a control system that controls a specified control target according to the results of inference using an inference model.
  • an information processing device includes a predictive distribution calculation unit that calculates a predictive distribution of a function indicating the relationship between parameters related to at least one of the inference and the control in a control system that controls a specific control object according to the result of inference by an inference model and the operating state of the control object during a period in which the parameters are applied, and a search unit that searches for candidates for the optimal value of the parameter based on the predictive distribution.
  • a search method is a search method executed by at least one information processing device, and includes the steps of: calculating a predictive distribution of a function indicating a relationship between a parameter relating to at least one of an inference and control in a control system that controls a specified control object according to the result of inference by an inference model, and the operating state of the control object during a period in which the parameter is applied; and searching for a candidate optimal value for the parameter based on the predictive distribution.
  • FIG. 1 is a diagram showing an overview of a control system according to an embodiment of the present invention.
  • 1 is a block diagram showing an example of a configuration of a main part of an information processing device (a device that searches for optimal parameter values) according to an embodiment of the present invention
  • 10A and 10B are diagrams illustrating a method for determining a threshold value and a method for determining whether or not input data is an outlier.
  • FIG. 1 is a diagram showing an overview of a control system according to an embodiment of the present invention.
  • 1 is a block diagram showing an example of a configuration of a main part of an information processing device (a device that searches for optimal
  • FIG. 1 is a diagram showing the transition of the calculated index value and the recurrence rate in an incineration power plant that generates power by utilizing heat generated by incinerating waste.
  • FIG. 13 is a diagram showing an example of a display screen of a search result.
  • a flowchart showing an example of processing performed by the information processing device (a device that calculates an index value indicating the suitability of an inference model).
  • 4 is a flowchart showing an example of a process executed by the information processing device (a device that searches for optimal values of parameters).
  • FIG. 1 is a diagram showing an overview of a control system 7 according to an embodiment of the present invention.
  • the control system 7 includes a learning device 1, an information processing device 2, an information processing device 3, a control device 4, and a control target 5.
  • the control system 7 is a system that controls the operation of the control target 5 by the control device 4, and the control of the control target 5 is performed based on the result of inference using an inference model.
  • the controlled object 5 is an object controlled by the control device 4 and includes equipment 51.
  • a measuring device 52 is also attached to the controlled object 5.
  • the measuring device 52 measures data related to the equipment 51, and may be adapted to suit the equipment 51 and the data to be measured. For example, if it is desired to measure the internal or external temperature of the equipment 51, a temperature sensor may be used as the measuring device 52.
  • the controlled object may include one of each of the equipment 51 and the measuring device 52, or multiple of each.
  • the equipment 51 is an equipment within the plant.
  • the measuring device 52 may be attached to the equipment 51, or may be attached elsewhere within the plant, or may be installed outside the plant.
  • the learning device 1 generates an inference model used to determine the content of control for the control target 5.
  • the inference model can also be called a machine learning model because it is generated by machine learning using training data.
  • the learning device 1 also re-learns the inference model and updates the inference model. Note that any machine learning algorithm can be used, and an appropriate algorithm can be applied depending on the training data used, the inference content, etc.
  • the inference model may be one that performs inference on matters that serve as guidelines for determining the content of control for the control object 5. For example, the inference model may predict whether the future operating state of the control object 5 will be normal or not. Also, for example, the inference model may predict future measurement values of the measuring device 52, or may predict the optimal content of control for the control object 5.
  • the information processing device 2 calculates an index value indicating the suitability of the inference model for input data input to the inference model generated by the learning device 1 when making an inference using the inference model. Details will be described later, but in calculating the index value, the information processing device 2 determines whether or not each of the multiple input data input to the inference model is an outlier, using a threshold value for determining whether or not training data included in the training dataset used to generate the inference model is an outlier. The information processing device 2 then calculates the index value based on the determination result. This makes it possible to accurately determine the suitability of the inference model for the input data. This index value is used to determine the timing for causing the learning device 1 to update the inference model.
  • the information processing device 3 searches for optimal values of parameters related to the control of the control object 5.
  • the information processing device 3 may also search for optimal values of parameters related to inference by an inference model instead of or in addition to the parameters related to the control of the control object 5.
  • the parameters detected by this search are provided to the control device 4, and the control device 4 applies these parameters to control the control object 5.
  • the information processing device 3 calculates a predictive distribution of a function that indicates the relationship between the above-mentioned parameters relating to at least one of the inference based on the inference model and the control according to the result of the inference, and the operating state of the control target 5 after the control.
  • the information processing device 3 searches for candidates for the optimal value of the parameters based on the calculated predictive distribution.This makes it possible to appropriately set the parameters without being influenced by the skill of the operator.
  • the control device 4 performs inference using the above-mentioned inference model, and determines the control content for the control target 5 based on the result of the inference.
  • the control device 4 includes a data acquisition unit 41, an inference unit 42, and a control content determination unit 43, and the above-mentioned functions are realized by each of these units.
  • the data acquisition unit 41 acquires data necessary for inference.
  • the data acquisition unit 41 may acquire measurement data measured by the measuring device 52.
  • the data acquired by the data acquisition unit 41 is used as input data for the inference model either directly or after undergoing a specified data processing.
  • the inference unit 42 then inputs the above-mentioned input data to the above-mentioned inference model to obtain output data.
  • the control content determination unit 43 determines the control content according to the result of inference by the inference unit 42, i.e., the output data described above. For example, if the inference model predicts whether or not the future operating state of the control target 5 will be normal, the control content determination unit 43 may not change the control content from the previous content when output data indicating that the operating state is likely to be normal is output. On the other hand, when output data indicating that the operating state is likely to be abnormal is output, the control content determination unit 43 may determine the control content for normalizing the operating state. The control content is determined based on parameters detected by the information processing device 3.
  • the data acquisition unit 41 acquires the updated inference model
  • the inference unit 42 inputs input data to the updated inference model to obtain output data
  • the control content determination unit 43 determines the control content for the control target 5 based on the output data.
  • the control system 7 includes an information processing device 2 that determines whether input data input to the inference model is an outlier using a threshold value for determining whether training data is an outlier, and calculates an index value indicating the suitability of the inference model for the input data based on the determination result, a learning device 1 that updates the inference model at a timing determined based on the calculated index value, and a control device 4 that determines the control content for the control target 5 based on output data obtained by inputting the input data to the updated inference model.
  • the information processing device 2 obtains the input data input to the updated inference model, and calculates an index value indicating the suitability of the inference model for the input data.
  • control system 7 can repeatedly re-learn and update the inference model and calculate an index value that indicates the compatibility between the inference model and the input data. This makes it possible to perform re-learning at an appropriate time and maintain the appropriateness of the control content for the control object 5.
  • the control system 7 includes an information processing device 3 that calculates a predictive distribution of a function that indicates the relationship between a certain parameter and the operating state of the control object 5 during a period in which the parameter is applied, and searches for a candidate optimal value for the parameter based on the calculated predictive distribution, and a control device 4 that applies the detected candidate optimal value to control the control object 5.
  • the certain parameter is a parameter related to at least either the control of the control object 5 or inference using an inference model.
  • the information processing device 3 acquires result data indicating the operating state during the period in which the detected candidate is applied, updates the predictive distribution based on the result data, and searches for new candidates for the optimal value of the parameter based on the updated predictive distribution. This allows the control system 7 to bring the parameter closer to the optimal value while controlling the control target 5.
  • [Configuration of information processing device 2] 2 is a block diagram showing an example of a main configuration of the information processing device 2.
  • the information processing device 2 includes a control unit 20 that controls each unit of the information processing device 2, and a storage unit 21 that stores various data used by the information processing device 2.
  • the information processing device 2 also includes a communication unit 22 that allows the information processing device 2 to communicate with other devices, an input unit 23 that accepts input of various data to the information processing device 2, and an output unit 24 that allows the information processing device 2 to output various data.
  • the control unit 20 also includes a data acquisition unit 201, an average distance calculation unit 202, a threshold determination unit 203, an outlier determination unit 204, an index value calculation unit 205, a relearning necessity determination unit 206, a recall calculation unit 207, and a learning data extraction unit 208.
  • the data acquisition unit 201 acquires various data used by the information processing device 2. For example, the data acquisition unit 201 acquires a training dataset including multiple training data used to generate an inference model, and input data input to the inference model for inference. For example, the data acquisition unit 201 may acquire a training dataset from the learning device 1 and acquire input data from the control device 4.
  • the average distance calculation unit 202 calculates the average distance between data. Specifically, the average distance calculation unit 202 performs a process for each training data to calculate the average value of the distance between one of the training data included in the training data set acquired by the data acquisition unit 201 and each of a predetermined number of training data that are closest to the training data. In addition, the average distance calculation unit 202 performs a process for each input data to calculate the average value of the distance between one of the multiple input data acquired by the data acquisition unit 201 and each of a predetermined number of other input data that are closest to the input data.
  • the threshold determination unit 203 determines a threshold for determining whether or not training data included in the training data set acquired by the data acquisition unit 201 is an outlier. More specifically, the threshold determination unit 203 calculates a deviation indicating the degree to which the training data included in the training data set deviates from other training data, for each training data included in the training data set, and determines a deviation of a predetermined rank from among the calculated deviations as the threshold.
  • the deviation may be, for example, an average value calculated by the average distance calculation unit 202 for each training data.
  • the outlier determination unit 204 determines whether each of the multiple input data acquired by the data acquisition unit 201 is an outlier. This determination is made using a threshold determined by the threshold determination unit 203. In other words, the outlier determination unit 204 determines whether the input data is an outlier based on the threshold determined by the threshold determination unit 203.
  • the index value calculation unit 205 calculates an index value that indicates the suitability of the inference model for the input data based on the judgment result of the outlier judgment unit 204.
  • the index value will be explained in the section [Examples of Index Values] below.
  • the relearning necessity determination unit 206 determines whether or not the inference model needs to be re-learned based on the index value calculated by the index value calculation unit 205. When the relearning necessity determination unit 206 determines that the inference model needs to be re-learned, it instructs the learning device 1 to re-learn the inference model. In other words, the timing at which the relearning necessity determination unit 206 determines that re-learning is necessary is the timing at which re-learning should be performed, and it can be said that the relearning necessity determination unit 206 determines the timing of re-learning.
  • the recall calculation unit 207 calculates the recall for each inference made by inputting multiple pieces of input data into the inference model.
  • the recall is an index showing the proportion of cases where the true value is predicted as a positive case among positive cases.
  • the recall can be calculated by the formula (number of cases correctly judged as abnormal) / ⁇ (number of cases correctly judged as abnormal) + (number of cases where an abnormality was erroneously judged as normal) ⁇ .
  • the recall calculation unit 207 calculates the recall for the judgment by dividing the number of cases correctly judged as abnormal by the sum of the number of cases correctly judged as abnormal and the number of cases where an abnormality was erroneously judged as normal.
  • the learning data extraction unit 208 extracts input data to be used for re-learning the inference model from among the multiple input data acquired by the data acquisition unit 201, based on the index value calculated by the index value calculation unit 205.
  • the index value calculated by the index value calculation unit 205 is calculated based on the judgment result of the outlier determination unit 204, so it can be said that the learning data extraction unit 208 extracts input data to be used for re-learning based on the judgment result of the outlier determination unit 204.
  • the learning data extraction unit 208 may also extract input data to be used for re-learning, taking into consideration the recall calculated by the recall calculation unit 207.
  • the learning data extraction unit 208 may transmit the extracted input data to the learning device 1, for example, when the re-learning necessity determination unit 206 determines that re-learning of the inference model is necessary.
  • the information processing device 2 includes an outlier determination unit 204 that determines whether each of a plurality of input data input to the inference model is an outlier using a threshold value for determining whether the training data included in the training dataset used to generate the inference model is an outlier, and an index value calculation unit 205 that calculates an index value indicating the suitability of the inference model for the input data based on the determination result of the outlier determination unit 204.
  • a threshold for determining whether training data is an outlier is used to determine whether input data input to the inference model is an outlier.
  • the above configuration which calculates an index value based on the above judgment result, makes it possible to calculate a valid index value.
  • this index value can be calculated without using the inference results from the inference model. Therefore, with the above configuration, it is possible to calculate a valid index value even when it is difficult to verify the correctness of the inference result, that is, to accurately determine the suitability of the inference model for the input data used in the inference. Furthermore, by using this index value, it is also possible to appropriately perform various processes related to updating the inference model.
  • the information processing device 2 is equipped with an outlier determination unit 204 that determines whether each of the multiple input data input to the inference model is an outlier using a threshold value for determining whether the training data included in the training dataset used to generate the inference model is an outlier, and a learning data extraction unit 208 that extracts data to be used for re-learning the inference model from the multiple input data input to the inference model based on the determination result of the outlier determination unit 204.
  • the learning data extraction unit 208 may extract input data using the index value calculated by the index value calculation unit 205, or may extract input data using the determination result of the outlier determination unit 204 without using the index value. In other words, it is not essential to have the index value calculation unit 205 when extracting input data to be used for re-learning.
  • FIG. 3 is a block diagram showing an example of a main configuration of the information processing device 3.
  • the information processing device 3 includes a control unit 30, a storage unit 31, a communication unit 32, an input unit 33, and an output unit 34, similar to the information processing device 2.
  • the control unit 30 of the information processing device 3 includes a data acquisition unit 301, an evaluation value calculation unit 302, a prediction distribution calculation unit 303, a search unit 304, an optimization control unit 305, and a display control unit 306.
  • the data acquisition unit 301 acquires various data used in the information processing device 3. Specifically, the data acquisition unit 301 acquires result data indicating the operating state of the control target 5 during the period in which control was performed using parameters detected by the information processing device 3 (more precisely, the search unit 304). For example, the data acquisition unit 301 may acquire measurement data from the measuring device 52 during that period as result data.
  • the evaluation value calculation unit 302 calculates an evaluation value that evaluates the operating state of the control target 5 using the result data acquired by the data acquisition unit 301.
  • the evaluation value may be anything that represents the quality of the operating state of the control target 5. It should be noted that this evaluation value can also be said to represent the quality of the applied parameters.
  • the evaluation value calculation unit 302 may use the abnormal time rate as the evaluation value.
  • the abnormal time rate is the ratio of the time during which the operating state of the controlled object 5 was abnormal to the operating time of the controlled object 5. Whether the operating state of the controlled object 5 is abnormal or normal can be determined based on the result data acquired by the data acquisition unit 301.
  • the predictive distribution calculation unit 303 calculates the predictive distribution of a function indicating the relationship between the above-mentioned parameters and the operating state of the control object 5 during the period in which the parameters are applied.
  • the operating state of the control object 5 is represented by an evaluation value calculated by the evaluation value calculation unit 302.
  • the evaluation value calculation unit 302 calculates the abnormal time rate
  • the predictive distribution calculation unit 303 uses the abnormal time rate as information indicating the operating state of the control object 5. In this case, it becomes possible to set the parameters to values that can reduce the abnormal time rate.
  • the evaluation value may be input via the input unit 33 or the like, and the evaluation value calculation unit 302 may be omitted.
  • the search unit 304 searches for candidates for the optimal parameter value based on the predictive distribution calculated by the predictive distribution calculation unit 303. Note that the method of calculating the predictive distribution and the method of searching for the candidates will be explained later in the section [Details of the method of calculating the predictive distribution and the method of searching for the optimal value candidates].
  • the optimization control unit 305 controls the parameter optimization performed by the predictive distribution calculation unit 303 and the search unit 304. For example, when a predetermined condition is satisfied, the optimization control unit 305 controls the predictive distribution calculation unit 303 and the search unit 304 to end the optimization.
  • the display control unit 306 causes the candidates detected by the search unit 304 to be displayed on a display device.
  • the display device may be a device provided in the information processing device 3.
  • the display control unit 306 may cause the candidates detected by the search unit 304 to be displayed on the output unit 34.
  • the display control unit 306 may also cause the candidates to be displayed on a display device connected to the information processing device 3, or to be displayed on a display device connected to that device via another device that can communicate via the communication unit 32.
  • the information processing device 3 includes a predictive distribution calculation unit 303 that calculates the predictive distribution of a function that indicates the relationship between the above-mentioned parameters and the operating state of the control target 5 during the period in which the parameters are applied, and a search unit 304 that searches for candidates for the optimal value of the parameters based on the calculated predictive distribution.
  • the above configuration makes it possible to detect appropriate parameters even when it is difficult to formulate the relationship between the parameters and the operating state. Therefore, the above configuration makes it possible to appropriately set parameters without being influenced by individual skills in a control system 7 that controls a specific control target according to the results of inference using an inference model.
  • the predictive distribution calculation unit 303 also updates the predictive distribution based on the candidates detected by the search unit 304 and the operating state of the control target 5 during the period in which the candidates are applied.
  • the search unit 304 searches for new candidates for the optimal parameter values based on the updated predictive distribution.
  • [Method of determining threshold and method of determining whether or not a value is an outlier] 4 is a diagram for explaining a method for determining a threshold and a method for determining whether input data is an outlier. In this section, a method for determining a threshold by the threshold determination unit 203 and a method for determining whether input data is an outlier by the outlier determination unit 204 will be explained based on FIG.
  • the threshold determination unit 203 determines the threshold using the training data set used for the machine learning of the inference model.
  • the average distance calculation unit 202 performs a process for each training data to calculate the average value of the distance between one piece of training data included in the training data set and each of a predetermined number of training data that are closest to the training data.
  • the average value calculated in this manner indicates the degree to which each training data deviates from the other training data, and can also be called the degree of deviation.
  • the left side of FIG. 4 shows plots in the feature space of each training data included in the training dataset.
  • the average distance calculation unit 202 calculates the distance between data D1 and each of the five training data that are closest to data D1 in the feature space, and calculates the average value.
  • the average distance calculation unit 202 calculates the average value of the distance between data D2 and each of the five training data that are closest to data D2.
  • the average distance calculation unit 202 performs this process for each training data included in the training dataset. These processes are similar to the processes performed in the k-nearest neighbor method, which is a method of data classification.
  • the threshold determination unit 203 sorts the average values calculated as described above in ascending order, finds the average value that has a predetermined rank, and determines this average value as the threshold.
  • the training data with a larger calculated average value deviates more from the other training data, and is more likely to be an outlier when viewed from the perspective of the entire training data set. For example, since the average value calculated for data D2 shown in FIG. 4 is larger than the average value calculated for data D1, data D2 is more likely to be an outlier than data D1.
  • the predetermined rank may be set as appropriate, and there is no particular limit to what value it is set to.
  • the predetermined rank may be set so that a predetermined percentage of training data in the training data set is ranked higher than the predetermined rank.
  • the value of (total number of training data) x 0.96 will be the predetermined rank. In this case, 4% of the training data will be outliers.
  • the outlier determination unit 204 uses the threshold determined as described above to determine whether or not the input data input to the inference model is an outlier. In determining whether or not an input data is an outlier, the average value is first calculated by the average distance calculation unit 202. More specifically, the average distance calculation unit 202 performs a process for each input data to calculate the average value of the distance between one piece of input data and each of a predetermined number of input data that are closest to the input data. The average value calculated by the average distance calculation unit 202 indicates the degree to which each input data deviates from the other input data, and can also be called the degree of deviation.
  • the predetermined number is 5, as in the example on the left side of the same figure.
  • the average distance calculation unit 202 calculates the distance between data d1 and each of the five input data that are closest to data d1 in the feature space, and calculates the average value of these.
  • the outlier determination unit 204 compares the calculated average value with the threshold value determined by the threshold value determination unit 203, and determines whether or not the data d1 is an outlier based on the magnitude relationship between the two values. For example, as shown in the figure, the outlier determination unit 204 may determine that the data d1 is an outlier when the average value calculated for the data d1 is equal to or greater than the threshold value. In the same manner, the outlier determination unit 204 determines whether or not each of the other input data is an outlier.
  • outliers may be detected by LOF (Local Outlier Factor).
  • the threshold determination unit 203 determines a threshold for the local density, and the outlier determination unit 204 determines that input data whose local density is below the threshold is an outlier.
  • outliers may be detected by Hotelling's theory.
  • the threshold determination unit 203 determines a threshold for the degree of abnormality, and the outlier determination unit 204 determines that input data whose degree of abnormality is above the threshold is an outlier.
  • the threshold determination unit 203 may calculate a deviation indicating the degree to which each piece of training data included in the training dataset deviates from other training data, and determine a deviation of a predetermined rank from among the calculated deviations as the threshold.
  • the outlier determination unit 204 may then determine whether each piece of input data is an outlier by comparing the deviation indicating the degree to which each piece of input data deviates from other input data, calculated for each piece of input data included in the plurality of input data, with the threshold determined by the threshold determination unit 203.
  • the method of calculating the deviation is not particularly limited.
  • the deviation may be the average distance from a certain number of nearby data.
  • the distance may be, for example, the Euclidean distance, or other distances such as the Manhattan distance or the Mahalanobis distance.
  • the degree of anomaly in Hotelling's theory may be used as the deviation, or the local density used in LOF may be used as the deviation.
  • the index value calculated by the index value calculation unit 205 may indicate the suitability of the inference model for the input data.
  • the index value calculation unit 205 may set the total number of input data determined to be outliers by the outlier determination unit 204 during a predetermined period as the index value. For example, when the control target 5 is operated every day, it is assumed that each of a plurality of input data used in inference during a day is determined to be an outlier or not. In this case, the index value calculation unit 205 may calculate the total number of input data determined to be outliers on a certain day as an index value indicating the suitability of the inference model at the end of that day.
  • the index value calculation unit 205 may calculate the index value using the number of input data that the outlier determination unit 204 has determined to be outliers. For example, the index value calculation unit 205 may calculate, as the index value, the proportion of input data that the outlier determination unit 204 has determined to be outliers among the input data measured by the measuring device 52 during a predetermined period.
  • the inference model is a model that predicts whether the future state of a specific object (e.g., a controlled object 5) will be normal or abnormal based on input data related to the object.
  • the index value calculation unit 205 may calculate, as an index value, the number of pieces of input data that were determined to be outliers and for which the state of the object was abnormal after a prediction using the input data, or a value calculated using the number of pieces of input data.
  • Input data that is determined to be an outlier can be classified into those in which the target state is abnormal after prediction using the input data and those in which the target state is normal. Of these, those in which the target state is abnormal after prediction can be said to contain characteristics that correspond to an abnormal state.
  • abnormal states do not change, but abnormal states vary. Therefore, when there are many outliers in input data that contains characteristics corresponding to abnormal states, it is possible that the abnormal state has changed since learning, and re-learning is highly necessary. Therefore, with the above configuration, it is possible to calculate an appropriate index value that takes into account the changing nature of the abnormal state.
  • the inference model may be a model that predicts whether a future state of a given object is normal or abnormal based on input data about the object. In this case, it is preferable to evaluate the accuracy of the inference model using the recall rate in order to reduce the possibility of overlooking an abnormal state, that is, of determining an abnormal state as normal when it is actually abnormal.
  • Figure 5 is a diagram showing the transition of the index value calculated by the index value calculation unit 205 and the recurrence rate in a waste incineration power plant that generates power by using heat generated by incinerating waste.
  • the index value is the total number of input data items determined to be outliers per day.
  • the figure also shows the transition of the set value of the steam volume in the waste incineration power plant.
  • the horizontal axis of the figure is time (days), and the vertical axis is normalized to a value between 0 and 1.
  • the index value increases and the recall rate decreases, even though there is no significant change in the setting value.
  • the amount of input data determined to be outliers increased and the recall rate decreased due to factors other than the change in the setting value.
  • a period in which the index value increases and the recall rate decreases in this way can be said to be a period in which the inference model has a low ability to classify normal from abnormal.
  • the recall calculation unit 207 may calculate the recall in a prediction made by inputting multiple input data into the inference model, i.e., the number of cases that were correctly determined to be abnormal divided by the sum of the number of cases that were correctly determined to be abnormal and the number of cases where an abnormality was erroneously determined to be normal.
  • a period when the recall rate is declining can be said to be a period when the accuracy of the inference model's predictions of whether something is normal or abnormal is declining, so recall rate can be used as an index showing the level of the inference model's ability to classify normal from abnormal.
  • recall rate can be used as an index showing the level of the inference model's ability to classify normal from abnormal.
  • the number of cases correctly determined to be abnormal and the number of cases where an abnormality is incorrectly determined to be normal, which are used to calculate recall rate are affected by intervention control based on the prediction results of the inference model and operator operations, so it is not desirable to use recall rate as an absolute evaluation index.
  • the above index value it is possible to identify periods when the input data used tends to be unlearned.
  • the learning data extraction unit 208 may extract input data to be used for re-learning the inference model from among the multiple input data, based on the recall calculated by the recall calculation unit 207 and the index value calculated by the index value calculation unit 205.
  • This makes it possible to estimate a period in particular that should be learned (a period in which the input data has a tendency to not be learned and has become an anomaly that should be avoided), to efficiently identify a data period that should be learned, and to extract input data from that period as data for re-learning.
  • effective re-learning can be performed using input data from the period indicated by circle C2 from among the acquired input data.
  • control parameters are:
  • the predictive distribution calculation unit 303 calculates the predictive distribution of a function indicating the relationship between the parameters to be optimized and the operating state of the controlled object after control, based on the result data acquired by the data acquisition unit 301. Note that this function will be referred to as the evaluation function f( ⁇ ) below. Furthermore, when new result data (for example, result data indicating the result of control in which a candidate detected by the search unit 304 is applied) is acquired, the predictive distribution calculation unit 303 updates the predictive distribution so that the result data is reflected.
  • new result data for example, result data indicating the result of control in which a candidate detected by the search unit 304 is applied
  • K * k( ⁇ , ⁇ )
  • f is expressed as follows.
  • the average function ⁇ ( ⁇ ) indicates the average value of the evaluation function predicted from the result data (and the evaluation value calculated by the evaluation value calculation unit 302 from the result data).
  • the variance function ⁇ ( ⁇ ) is the variance of the evaluation function predicted from the result data (and the evaluation value calculated by the evaluation value calculation unit 302 from the result data).
  • ⁇ ( ⁇ ) indicates the uncertainty of the prediction, and its value tends to be large in areas where result data is insufficient. If ⁇ is large, it can be said that the prediction is uncertain, and that the result data required to increase the certainty of the prediction is insufficient.
  • the kernel function and the parameter ⁇ k of the kernel function included in the variance function ⁇ ( ⁇ ) affect the calculation of the predictive distribution. When the predictive distribution is calculated, the parameter ⁇ k is optimized.
  • the optimization method is not particularly limited, and various optimization methods applied in general Bayesian optimization can be applied.
  • the search unit 304 searches for optimal control parameter candidates in order to obtain the optimal control parameters. Specifically, the search unit 304 searches for parameters that maximize the acquisition function a( ⁇ ) using the mean function ⁇ ( ⁇ ) and the variance function ⁇ ( ⁇ ), as shown in equations (4) and (5) below. The parameters detected in this search become optimal parameter candidates. This search is based on the UCB (Upper Confidence Bound) strategy.
  • ⁇ is a parameter for adjusting search and utilization.
  • the search unit 304 sets ⁇ to a value according to the received selection and searches for candidates.
  • optimal parameter candidates may be searched for using the PI (Probability of Improvement) strategy or the EI (Expected Improvement) strategy.
  • optimal parameter candidates may be searched for using an acquisition function appropriate for each strategy, for example, by applying a strategy such as PTR (Probability in Target Range) or MI (Mutual Information).
  • the optimal parameters are those that minimize the value of the evaluation function (for example, when the abnormal time rate is used as information indicating the operating state), it is sufficient to search for parameters that minimize the acquisition function a( ⁇ ).
  • the candidate optimal values for the parameters detected by the search unit 304 may be presented to the operator of the information processing device 3 by the display control unit 306 displaying them on a display device, etc. Then, the control device 4 starts control of the control target 5 to which the candidate has been applied, and the measurement data measured by the measuring device 52 during the period in which the control is performed is acquired and stored by the control device 4. The applied parameters are then associated with the stored measurement data, and the data is input to the information processing device 3 as new result data.
  • the predictive distribution calculation unit 303 updates the predictive distribution based on the new result data
  • the search unit 304 searches for the optimal value of the parameter based on an evaluation function constructed based on the predictive distribution updated by the predictive distribution calculation unit 303. In this way, by repeating the updating of the predictive distribution and the search for the optimal value, it becomes possible to detect the optimal parameter.
  • the display control unit 306 may present the search results to the operator by displaying a display screen such as that shown in Fig. 6.
  • Fig. 6 is a diagram showing an example of a display screen showing the search results. More specifically, Fig. 6 is a diagram showing, on a parallel coordinate system, the history of candidates detected for each of elements a to d included in the parameters to be searched.
  • the parameters to be searched may include multiple elements, which allows multiple elements to be optimized in parallel.
  • the above elements a to d may be various control parameters related to automatic combustion control.
  • control parameters related to automatic combustion control include parameters related to adjustment of the combustion speed, adjustment of the dry air supply flow rate, adjustment of the combustion air supply flow rate, adjustment of the combustion air temperature, and adjustment of the height of the waste layer to be incinerated.
  • the parameters to be searched may also include, for example, parameters indicating the content and amount of control of the control target 5 according to the result of inference by the inference model. This makes it possible to appropriately set the content and amount of control according to the result of inference by the inference model.
  • the parameters to be searched may include, in addition to the above parameters indicating the content and amount of control over the control object 5, inference parameters used for inference by the inference model.
  • the search unit 304 searches for both candidates for the optimal values of the content and amount of control over the control object 5 and candidates for the optimal values of the inference parameters based on the predictive distribution calculated by the predictive distribution calculation unit 303.
  • the inference parameters may be hyperparameters such as weight values obtained by learning.
  • the inference parameters may be the ensemble ratio (weight for each inference result).
  • the operating state of the control object 5 is affected by the results of inference using the inference model and by intervention control based on those results. For this reason, if the optimum value of the control content and control amount for the control object 5 and the optimum value of the inference parameters are calculated separately, the parameter combination that is optimal overall may not be obtained. In this regard, with the above configuration, both the optimum value of the control content and control amount for the control object 5 and the optimum value of the inference parameters are searched for, making it possible to detect the optimum parameter combination overall.
  • the output value of the inference model indicates the probability of normality and/or the probability of abnormality. Therefore, in order for the control content decision unit 43 in the control device 4 to decide the control content according to whether it is normal or abnormal, it is necessary to determine whether it is normal or abnormal using a threshold value for the above probability.
  • the above elements a to d may include a threshold for determining whether the future state of the control target 5 is normal or abnormal. This makes it possible to properly determine whether it is normal or abnormal, and to carry out appropriate control according to the determination result. Note that the number of elements included in the parameters to be optimized is arbitrary, and the content of each element is not limited to the above example.
  • the vertical axis represents the value of each element included in the parameters.
  • a graph is then shown on the parallel coordinate system, with broken lines connecting the elements included in the parameters detected in one search. In other words, the intersections of the broken lines and the vertical axis represent the values of each element.
  • This type of graph is called a parallel coordinate plot.
  • the parallel coordinate plot shown in Figure 6 shows the transitions in the values of the candidates detected in each search by search unit 304, and can be called transition information.
  • the display control unit 306 may display transition information indicating the transition of the candidate values detected in each search by the search unit 304. This allows the operator to easily recognize how the candidate optimal values for the parameters have transitioned through repeated searches, and to confirm whether the optimization is proceeding normally.
  • the display control unit 306 may display a parallel coordinate plot, i.e., a graph showing the values of the candidates detected in each search for each element, on parallel coordinates. This allows the operator to easily recognize how each candidate detected for each element included in the parameters has changed as the search is repeated, and to confirm whether the optimization is proceeding normally.
  • the parallel coordinate plot is merely one example of a display format for the transition information, and the display format for the transition information is arbitrary.
  • the display control unit 306 may display, as the transition information, a parallel set graph, a contour plot, a correlograph, or the like, which shows the transition of the values of the candidates detected in each search by the search unit 304.
  • the lines constituting the line graph include solid lines, dashed lines, and dashed lines.
  • the patterns of these lines correspond to the operating state of the control object 5 after the control object 5 is controlled by applying the values of each element on the line.
  • the lines connecting elements when the abnormal time rate is less than the first threshold are solid lines
  • the lines connecting elements when the abnormal time rate is equal to or greater than the first threshold and less than the second threshold are dashed lines
  • the lines connecting elements when the abnormal time rate is equal to or greater than the second threshold are dashed lines.
  • the display control unit 306 may change the display mode of the value of each candidate included in the transition information depending on the operating state of the control target 5 after the control of the control target 5 is performed by applying the detected candidate. This allows the good and bad of each candidate to be easily recognized.
  • the display mode according to the operating state is arbitrary and is not limited to the example shown in the figure.
  • the display control unit 306 may display the value of each candidate in a color according to the operating state. In this case, it is sufficient to determine in advance the correspondence between the evaluation value (e.g., abnormal time rate) that evaluates the operating state and the display color. This allows the display control unit 306 to display the value of each candidate in a display color according to the evaluation value calculated by the evaluation value calculation unit 302.
  • Fig. 7 is a flowchart showing an example of the process executed by the information processing device 2.
  • the data acquisition unit 201 acquires a training dataset.
  • the method of acquiring the training dataset is arbitrary.
  • the data acquisition unit 201 may acquire a training dataset input via the input unit 23, or may acquire a training dataset from the learning device 1 via the communication unit 22.
  • the average distance calculation unit 202 calculates, for each training data included in the training data set acquired in S11, the average value of the distance between the training data and a predetermined number of training data in the vicinity of the training data.
  • the predetermined number of training data in the vicinity of a certain training data refers to a predetermined number of other training data that are closest in distance to the training data.
  • the threshold determination unit 203 determines the average value of a predetermined rank among the multiple average values calculated in S12 as the threshold value for determining whether the input data is an outlier. Note that the processes of S11 to S13 should be performed at the latest by the time the process of S16 is performed, and do not necessarily have to be performed immediately before S14.
  • the data acquisition unit 201 acquires the input data used for inference by the inference model.
  • the method of acquiring the input data is arbitrary.
  • the data acquisition unit 201 may acquire the input data input via the input unit 23.
  • the data acquisition unit 201 may acquire the input data from the control device 4 via the communication unit 22, or may acquire measurement data from the measurement device 52 to generate the input data.
  • the data acquisition unit 201 may acquire input data for a predetermined period of time all at once, or may acquire the input data in real time, that is, at the time when the input data is used or will be used in inference by the inference model.
  • the average distance calculation unit 202 calculates the average value of the distance between the input data acquired in S14 and a predetermined number of input data in the vicinity of the input data. If one input data is acquired in S14, the average distance calculation unit 202 calculates the average value of the distance between the input data and the multiple input data acquired previously. On the other hand, if multiple input data are acquired in S14, the average distance calculation unit 202 performs the process of calculating the average value of the distance between the input data and a predetermined number of input data in the vicinity of the input data for each of the multiple acquired input data.
  • the outlier determination unit 204 determines whether or not the input data acquired in S14 is an outlier based on the average value calculated in S15. More specifically, the outlier determination unit 204 compares the average value calculated in S15 with the threshold value determined in S13, and determines whether or not the input data is an outlier based on the comparison result. Note that if multiple pieces of input data are acquired in S14, the outlier determination unit 204 determines for each piece of input data whether or not the input data is an outlier.
  • the index value calculation unit 205 calculates an index value indicating the suitability of the inference model for the input data based on the determination result of S16. For example, the index value calculation unit 205 may calculate, as the index value, the number of input data that are determined to be outliers in S16 among the input data corresponding to the measurement data measured by the measuring device 52 during a predetermined period.
  • the relearning necessity determination unit 206 determines whether relearning is necessary based on the index value calculated in S17. For example, the relearning necessity determination unit 206 may determine that relearning is necessary (YES in S18) if the index value calculated in S17 is equal to or greater than a predetermined threshold value, and may determine that relearning is not necessary (NO in S18) if the index value is less than the threshold value. If NO is determined in S18, the processing in FIG. 7 ends. Note that when input data is acquired in real time, if NO is determined in S18, the process may return to S14 and new input data may be acquired.
  • the process proceeds to S19, where the re-learning necessity determination unit 206 transmits the input data acquired in S14 to the learning device 1 to re-learn the inference model, and the process in FIG. 7 ends.
  • the reproducibility calculation unit 207 may calculate the reproducibility of the inference performed using the input data acquired in S14.
  • the learning data extraction unit 208 may extract input data to be used for re-learning the inference model from the multiple input data acquired in S14 based on the index value calculated in S17 and the reproducibility calculated by the reproducibility calculation unit 207.
  • the learning data extraction unit 208 may transmit the input data to be used for re-learning to the learning device 1 when the determination in S18 is YES.
  • the relearning necessity determination unit 206 may perform a process of prompting the operator to relearn the inference model instead of controlling the learning device 1 to perform relearning.
  • the relearning necessity determination unit 206 functions as a notification unit that prompts the operator to relearn the inference model according to the determination result of the outlier determination unit 204.
  • the notification mode and the notification target are not particularly limited.
  • the notification unit 210 may notify by outputting information indicating that relearning is necessary.
  • the output destination is not particularly limited, and for example, the notification unit 210 may output the information to the output unit 24, may output the information to the learning device 1, or may output the information to another device such as a terminal device owned by the operator.
  • the index value calculation unit 205 may be omitted, and the relearning necessity determination unit 206 may determine whether relearning is necessary using the determination result of the outlier determination unit 204. For example, the relearning necessity determination unit 206 may count the number of input data items determined to be outliers in S16, and determine that relearning is necessary if the counted number is equal to or greater than a predetermined threshold value.
  • the information processing device 2 may be configured to include an outlier determination unit 204 that determines whether or not each of a plurality of input data input to the inference model is an outlier using a threshold value for determining whether or not the training data included in the training dataset used to generate the inference model is an outlier, and a relearning necessity determination unit (alert unit) 206 that prompts relearning of the inference model depending on the determination result of the outlier determination unit 204.
  • an outlier determination unit 204 that determines whether or not each of a plurality of input data input to the inference model is an outlier using a threshold value for determining whether or not the training data included in the training dataset used to generate the inference model is an outlier
  • a relearning necessity determination unit (alert unit) 206 that prompts relearning of the inference model depending on the determination result of the outlier determination unit 204.
  • the information processing device 2 may also include a learning unit that performs re-learning of the inference model.
  • the learning device 1 can be omitted from the control system 7.
  • re-learning is performed by the learning unit included in the information processing device 2.
  • the index value calculation unit 205 may be omitted.
  • the timing of re-learning is determined using the judgment result of the outlier judgment unit 204.
  • the relearning necessity judgment unit 206 may judge whether or not re-learning is necessary using the judgment result of the outlier judgment unit 204, in which case the time when it is judged that re-learning is necessary becomes the timing of re-learning.
  • the relearning necessity judgment unit 206 may be omitted and the learning unit may judge whether or not to re-learn, in which case the learning unit will determine the timing of re-learning based on the judgment result of the outlier judgment unit 204.
  • the information processing device 2 may be configured to include an outlier determination unit 204 that determines whether or not each of a plurality of input data input to the inference model is an outlier using a threshold value for determining whether or not the training data included in the training dataset used to generate the inference model is an outlier, and a learning unit that re-learns the inference model at a timing determined based on the determination result of the outlier determination unit 204.
  • a threshold for determining whether training data is an outlier is used to determine whether input data input to the inference model is an outlier.
  • Fig. 8 is a flowchart showing an example of the process executed by the information processing device 3.
  • the timing for executing the process in Fig. 8 is not particularly limited.
  • the process may be executed at predetermined intervals, or may be executed at a timing when it is determined that the operating state of the control target 5 has deteriorated or is showing a tendency to deteriorate based on information such as an abnormal time rate indicating the operating state of the control target 5.
  • the data acquisition unit 301 acquires the initial values of the parameters to be optimized and the result data corresponding to the initial values.
  • the data acquisition unit 301 may acquire the initial values and result data input via the input unit 33.
  • the result data corresponding to the initial values of the parameters indicates the operating state of the control object 5 during the period in which the control of the control object 5 was performed using the initial values, and may be, for example, measurement values (measurement data) measured by the measuring device 52 during that period.
  • the data acquisition unit 301 may acquire, in addition to the initial value and the resulting data, an upper limit value of the parameter and the resulting data when the upper limit value is applied, and a lower limit value of the parameter and the resulting data when the lower limit value is applied.
  • the method for determining the initial parameter values is not particularly limited.
  • the initial parameter values may be determined by descriptive statistical methods or the like.
  • a frequency distribution table showing the distribution of parameter values before and after the occurrence of an abnormality may be created, and based on that, a parameter value considered to be optimal may be identified, and the identified value may be used as the initial value.
  • the parameter value may be set to the initial value, and control of the control target 5 may be performed for a predetermined period, and result data showing the operating state during that period may be obtained.
  • the method of determining the upper and lower limits of the parameters may be determined by descriptive statistical methods, as with the initial values, or the initial value may be used as a reference and values that deviate from the initial value by a specified range may be set as the upper and lower limits.
  • the specified range may be set to 10%, with the upper limit being determined by adding 10% of the initial value to the initial value, and the lower limit being determined by subtracting 10% of the initial value from the initial value. Result data can be obtained for each of the upper and lower limits in the same manner as for the initial values.
  • the evaluation value calculation unit 302 uses the result data acquired in S21 to calculate an evaluation value that evaluates the operating state of the control target 5 during the period in which the control target 5 was controlled by applying the initial value.
  • the evaluation value for example, the abnormal time rate described above may be applied. Note that the definition of "abnormality" when calculating the abnormal time rate may be the same as that when learning the inference model, or a broader definition may be applied.
  • the predictive distribution calculation unit 303 calculates the predictive distribution of a function indicating the relationship between the parameters to be optimized and the operating state of the control target 5 based on the initial values acquired in S21 and the evaluation values calculated in S22.
  • the display control unit 306 may present the calculated predictive distribution to the operator by, for example, outputting it to the output unit 34.
  • the search unit 304 searches for candidates for the optimal parameter value based on the predictive distribution calculated in S23.
  • the display control unit 306 presents the detected candidates to the operator by, for example, outputting the candidates to the output unit 34.
  • the operator checks whether there is any problem with applying the presented candidate, and if it is determined that there is no problem, inputs the candidate into the control device 4 and starts control of the control object 5 applying the candidate.
  • the operator then inputs the measurement data etc. measured by the measuring device 52 during the period in which control applying the candidate was performed, together with the values of the applied parameters (the values of the presented candidates) into the information processing device 3 as result data indicating the operating state of the control object 5.
  • the search unit 304 may search for a candidate different from the previously detected candidate. Also, a normal range for the parameters may be determined in advance. In this case, regardless of the operator's judgment, if the candidate detected in S24 is outside the normal range, the processing from S25 onwards is skipped.
  • the data acquisition unit 301 acquires the applied parameter values and result data input as described above.
  • the data acquisition unit 301 may acquire the applied parameter values and result data from the control device 4.
  • the evaluation value calculation unit 302 calculates an evaluation value using the result data acquired in S25.
  • the optimization control unit 305 determines whether or not to end the optimization based on the evaluation value calculated in S26. For example, the optimization control unit 305 may determine to end the optimization (YES in S27) if the evaluation value is equal to or greater than a predetermined threshold, and may determine to continue the optimization (NO in S27) if the evaluation value is less than the threshold. If YES is determined in S27, the processing of FIG. 8 ends, and if NO is determined in S27, the processing returns to S23. In S23, which is transitioned from S27, the predictive distribution is updated using the parameter values and result data acquired in S25.
  • the execution subject of each process described in each of the above-mentioned embodiments is arbitrary and is not limited to the above-mentioned examples.
  • the functions of the learning device 1, the information processing devices 2 and 3, and the control device 4 can be realized by a plurality of information processing devices (which can also be called processors) that can communicate with each other.
  • each process described in the flowcharts of Figures 7 and 8 can be shared among a plurality of information processing devices.
  • the execution subject of the control method in each of the above-mentioned embodiments may be one information processing device or a plurality of information processing devices.
  • the functions of the learning device 1 may be provided in the information processing device 2 and these may be integrated into a single device, or some or all of the functions of the learning device 1, the information processing device 2, and the information processing device 3 may be provided in the control device 4. In this way, it is possible to appropriately change the types of devices that constitute the control system 7.
  • the functions of the learning device 1, information processing devices 2, 3, and control device 4 can be realized by a program for causing a computer to function as the device, and a program (index value calculation program/search program) for causing a computer to function as each control block of the device (particularly each part included in the control unit 20 and the control unit 30).
  • the device includes a computer having at least one control device (e.g., a processor) and at least one storage device (e.g., a memory) as hardware for executing the program.
  • control device e.g., a processor
  • storage device e.g., a memory
  • the program may be recorded on one or more computer-readable recording media, not on a temporary basis.
  • the recording media may or may not be included in the device. In the latter case, the program may be supplied to the device via any wired or wireless transmission medium.
  • each of the above control blocks can be realized by a logic circuit.
  • a logic circuit for example, an integrated circuit in which a logic circuit that functions as each of the above control blocks is formed is also included in the scope of the present invention.
  • An information processing device includes a predictive distribution calculation unit that calculates a predictive distribution of a function indicating the relationship between parameters relating to at least one of an inference and the control in a control system that performs control on a specified controlled object in accordance with the result of an inference using an inference model and the operating state of the controlled object during a period in which the parameters are applied, and a search unit that searches for candidates for an optimal value of the parameter based on the predictive distribution.
  • the parameters indicate the content and amount of control of the control object according to the result of the inference.
  • the parameters include inference parameters used for inference by the inference model
  • the search unit searches for both the content of control and the optimal value of the control amount for the control object, and the optimal value of the inference parameters, based on the predictive distribution.
  • the inference model predicts a value indicating the future operating state of the control object, and the parameter is a threshold value for determining whether the future operating state of the control object is normal or not.
  • the inference model predicts a value indicating the future operating state of the control object
  • the predictive distribution calculation unit uses the ratio of the time during which the operating state of the control object was abnormal to the operating time of the control object as information indicating the operating state of the control object.
  • the predictive distribution calculation unit updates the predictive distribution based on the candidate detected by the search unit and the operating state of the control target during the period in which the candidate is applied, and the search unit searches for a new candidate for the optimal value of the parameter based on the updated predictive distribution.
  • the information processing device is the same as in aspect 6, but includes a display control unit that displays transition information indicating the transition of the candidate values detected in each search by the search unit.
  • the display control unit changes the display mode of the value of each candidate included in the transition information depending on the operating state of the control object after applying the candidate to control the control object.
  • a control system includes the information processing device according to aspect 1, and a control device that applies candidates for optimal values of the parameters detected by the information processing device to control the control target, and the information processing device obtains result data indicating the operating state during the period in which the candidates are applied, updates the predictive distribution based on the result data, and searches for new candidates for optimal values of the parameters based on the updated predictive distribution.
  • the search method according to aspect 10 of the present invention is a search method executed by at least one information processing device, and includes the steps of: calculating a predictive distribution of a function indicating the relationship between a parameter relating to at least one of the inference and the control in a control system that controls a specified control object according to the result of inference by an inference model, and the operating state of the control object during a period in which the parameter is applied; and searching for a candidate optimal value for the parameter based on the predictive distribution.
  • the search program according to aspect 11 of the present invention is a search program for causing a computer to function as the information processing device described in claim 1, and causes the computer to function as the predictive distribution calculation unit and the search unit.

Landscapes

  • Testing And Monitoring For Control Systems (AREA)
  • Incineration Of Waste (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

L'objectif de la présente invention est de définir un paramètre relatif à l'inférence et à la commande de manière appropriée quelle que soit la compétence technique d'un individu. Un dispositif de traitement d'informations (3) comprend : une unité de calcul de distribution prédite (303) pour calculer une distribution prédite d'une fonction représentant une relation entre un paramètre d'un système de commande (7) qui commande un objet commandé (5) conformément au résultat d'une inférence obtenue par un modèle d'inférence, et un état de fonctionnement de l'objet commandé (5) dans une période dans laquelle le paramètre est appliqué ; et une unité de recherche (304) pour rechercher un candidat d'une valeur optimale du paramètre sur la base de la distribution prédite.
PCT/JP2023/043618 2022-12-12 2023-12-06 Dispositif de traitement d'informations, système de commande, procédé de recherche et programme de recherche WO2024128090A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
JP2022-198178 2022-12-12
JP2022198178A JP2024084021A (ja) 2022-12-12 2022-12-12 情報処理装置、制御システム、探索方法、および探索プログラム

Publications (1)

Publication Number Publication Date
WO2024128090A1 true WO2024128090A1 (fr) 2024-06-20

Family

ID=91484943

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/JP2023/043618 WO2024128090A1 (fr) 2022-12-12 2023-12-06 Dispositif de traitement d'informations, système de commande, procédé de recherche et programme de recherche

Country Status (2)

Country Link
JP (1) JP2024084021A (fr)
WO (1) WO2024128090A1 (fr)

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2005249349A (ja) * 2004-03-05 2005-09-15 Ebara Corp 廃棄物処理プラント設備の運転制御方法及び運転制御装置
JP2021135940A (ja) * 2020-02-28 2021-09-13 日立造船株式会社 情報処理装置、制御システム、制御変数決定方法、および制御変数決定プログラム

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2005249349A (ja) * 2004-03-05 2005-09-15 Ebara Corp 廃棄物処理プラント設備の運転制御方法及び運転制御装置
JP2021135940A (ja) * 2020-02-28 2021-09-13 日立造船株式会社 情報処理装置、制御システム、制御変数決定方法、および制御変数決定プログラム

Also Published As

Publication number Publication date
JP2024084021A (ja) 2024-06-24

Similar Documents

Publication Publication Date Title
US20220146993A1 (en) Machine learning method and machine learning device for learning fault conditions, and fault prediction device and fault prediction system including the machine learning device
CN108875784B (zh) 用于工业中的性能指标的基于数据的优化的方法和***
Chen et al. Evidential KNN-based condition monitoring and early warning method with applications in power plant
Khelif et al. RUL prediction based on a new similarity-instance based approach
Zhao et al. An improved case-based reasoning method and its application on fault diagnosis of Tennessee Eastman process
JP6333868B2 (ja) セル制御装置、及び製造セルにおける複数の製造機械の稼働状況を管理する生産システム
JP6896590B2 (ja) 予知モデル維持システム、予知モデル維持方法及び予知モデル維持プログラム
Serdio et al. Improved fault detection employing hybrid memetic fuzzy modeling and adaptive filters
CN116757534A (zh) 一种基于神经训练网络的智能冰箱可靠性分析方法
JP7481902B2 (ja) 管理計算機、管理プログラム、及び管理方法
JP2021090344A (ja) 機械学習装置、デマンド制御システム、及び、空調制御システム
Castano et al. Data-driven insights on time-to-failure of electromechanical manufacturing devices: a procedure and case study
CN117639602A (zh) 电机运行状态自适应调节方法及***
WO2024128090A1 (fr) Dispositif de traitement d'informations, système de commande, procédé de recherche et programme de recherche
WO2022054256A1 (fr) Dispositif de détection d'anomalie
Rasay et al. Reinforcement learning based on stochastic dynamic programming for condition-based maintenance of deteriorating production processes
WO2024128089A1 (fr) Dispositif de traitement d'informations, système de commande, procédé et programme de calcul de valeur d'indice
CN109829115B (zh) 搜索引擎关键词优化方法
Zemouri et al. Autonomous and adaptive procedure for cumulative failure prediction
GB2465861A (en) A reasoning inference making tool for recommending actions based on a hybridisation of a data driven model and knowledge based logic.
Mejri et al. A new time adjusting control limits chart for concept drift detection
Yasenjiang et al. Fault Diagnosis and Prediction of Continuous Industrial Processes Based on Hidden Markov Model‐Bayesian Network Hybrid Model
WO2022004039A1 (fr) Procédé d'entraînement de modèle prédictif, dispositif d'entraînement de modèle prédictif et système de commande d'installation
Fernandez-Anakabe et al. An attribute oriented induction based methodology for data driven predictive maintenance
JPH0695880A (ja) 事例ベース推論装置