CN116086537A - Equipment state monitoring method, device, equipment and storage medium - Google Patents
Equipment state monitoring method, device, equipment and storage medium Download PDFInfo
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Abstract
The application discloses a method, a device, equipment and a storage medium for monitoring equipment states, which relate to the technical field of state monitoring and comprise the following steps: acquiring sensor data detected by each sensor measuring point of target equipment to obtain monitoring data to be evaluated; setting elements in an influence degree matrix according to the influence degree of the sensor measuring points on the state of the target equipment; calculating a distance value between the monitoring data to be evaluated and the historical monitoring data by using the set influence matrix; calculating an evaluation value of the sensor measuring point by using the distance value and the historical monitoring data; calculating a residual value between the evaluation value and the monitoring data to be evaluated; and monitoring and evaluating the state of the target equipment according to the residual value and the upper limit and the lower limit of the residual value. According to the method and the device, the relative influence degree between the non-important sensor measuring points and the important sensor measuring points is adjusted by using the adjustable influence degree matrix, so that the negative influence of the non-important sensor measuring points on the important sensor measuring points can be reduced, and a more accurate comprehensive equipment evaluation result is obtained.
Description
Technical Field
The present disclosure relates to the field of state monitoring technologies, and in particular, to a method, an apparatus, a device, and a storage medium for monitoring a device state.
Background
With the rapid development and progress of industry, the equipment in industry is continuously developed towards automation, informatization and intelligence. During the operation of the devices, abnormality or failure occurs from time to time, which not only damages the devices to cause property loss, but also affects the operation and efficiency of industrial production, and also causes personal harm to operators around the devices. Therefore, in order to ensure stable operation of the mechanical equipment, reduce potential safety hazards and ensure smooth production, state monitoring and evaluation of the mechanical equipment are required.
At present, the equipment state monitoring and evaluation has successful application in the aspects of nuclear power station sensor verification, equipment monitoring, electronic product life prediction and the like, and the estimation of the current actual monitoring data is obtained by analyzing and comparing the actual monitoring data with the health data of the equipment in normal operation and calculating the data in normal operation based on the historical health data.
Currently, the mainstream device state monitoring methods include a method based on a memory matrix, a method based on MSET (Multivariate State Estimation Technique ), and a method based on ANN (Artificial Neural Network, artificial neural network). However, when the current state monitoring method monitors the multi-dimensional state of the equipment through the monitoring group formed by the plurality of groups of sensor measuring points, the lack of identification on the influence degree of the sensor measuring points easily causes that the non-important sensor measuring points influence the important sensor measuring points, and the state evaluation of the equipment is easily negatively influenced. It can be understood that when the device is comprehensively evaluated by using multidimensional data between sensor measuring points, the evaluation accuracy of important measuring points is affected by unimportant measuring points so as to reduce the effectiveness of the comprehensive evaluation result, for example, when the fluctuation of the unimportant sensor measuring points is large (the inlet water temperature of the water pump) and the fluctuation of the important sensor measuring points is not large (the current of the water pump motor), the overall evaluation result of the device is driven to have large fluctuation, so that misdiagnosis occurs.
In summary, how to monitor the status of the device, avoiding the influence of non-important sensor measurement points on important sensor measurement points, and negatively affecting the evaluation of the status of the device are problems that those skilled in the art need to solve at present.
Disclosure of Invention
In view of the foregoing, an object of the present application is to provide a method, an apparatus, a device, and a storage medium for monitoring a device state, which can reduce the negative influence of non-important sensor measurement points on important sensor measurement points, and obtain a more accurate device comprehensive evaluation result. The specific scheme is as follows:
in a first aspect, the present application discloses a device status monitoring method, including:
acquiring sensor data detected by each sensor measuring point of the target equipment at present to obtain monitoring data to be evaluated;
setting elements in a pre-established influence degree matrix according to the influence degree of each sensor measuring point on the state of the target equipment to obtain a set influence degree matrix;
calculating the distance between the monitoring data to be evaluated and the historical monitoring data collected under the normal running state of the target equipment by using the set influence matrix to obtain a distance value;
calculating an evaluation value corresponding to each sensor measuring point by using the distance value and the historical monitoring data;
calculating residual errors between the evaluation value and the monitoring data to be evaluated to obtain residual values;
and monitoring and evaluating the state of the target equipment according to the residual value and the preset upper and lower limits of the residual value.
Optionally, the calculating, using the distance value and the historical monitoring data, an evaluation value corresponding to each sensor measurement point includes:
and converting the distance value into a weight value, and calculating an evaluation value corresponding to the sensor measuring point by using the weight value and the historical monitoring data.
Optionally, the converting the distance value into a weight value includes:
the distance value is converted into a weight value using a gaussian kernel function.
Optionally, the device state monitoring method further includes:
collecting historical sensor data detected by each sensor measuring point in the normal running state of the target equipment to obtain historical sensor data;
and processing the historical sensor data to obtain historical monitoring data.
Optionally, the processing the historical sensor data to obtain historical monitoring data includes:
normalizing the historical sensor data to obtain normalized data;
and removing abnormal values in the normalized data to obtain historical monitoring data.
Optionally, the setting the elements in the pre-created influence degree matrix according to the influence degree of each sensor measurement point on the state of the target device to obtain the set influence degree matrix includes:
dividing the sensor measuring points according to the influence degree of each sensor measuring point on the state of the target equipment to obtain non-important sensor measuring points and important sensor measuring points;
setting corresponding adjustment parameters for the non-important sensor measuring points and the important sensor measuring points according to the influence degree to obtain non-important sensor adjustment parameters and important sensor adjustment parameters;
and setting elements in the pre-established influence degree matrix by using the non-important sensor adjustment parameters and the important sensor adjustment parameters to obtain the set influence degree matrix.
Optionally, the setting the elements in the pre-created influence degree matrix by using the non-important sensor adjustment parameters and the important sensor adjustment parameters to obtain a set influence degree matrix includes:
reducing elements corresponding to the non-important sensor measuring points in a pre-created influence degree matrix by using the non-important sensor adjusting parameters to obtain a set influence degree matrix;
and/or, using the important sensor adjustment parameters to adjust elements corresponding to the important sensor measuring points in the influence degree matrix, so as to obtain the set influence degree matrix.
In a second aspect, the present application discloses an apparatus status monitoring device, comprising:
the data acquisition module is used for acquiring sensor data detected by each sensor measuring point of the target equipment at present to obtain monitoring data to be evaluated;
the element setting module is used for setting elements in a pre-established influence degree matrix according to the influence degree of each sensor measuring point on the state of the target equipment to obtain a set influence degree matrix;
the distance calculation module is used for calculating the distance between the monitoring data to be evaluated and the historical monitoring data collected under the normal running state of the target equipment by using the set influence matrix to obtain a distance value;
the evaluation value calculation module is used for calculating the evaluation value corresponding to each sensor measuring point by using the distance value and the historical monitoring data;
the residual calculation module is used for calculating residual errors between the evaluation value and the monitoring data to be evaluated to obtain a residual value;
and the monitoring and evaluating module is used for monitoring and evaluating the state of the target equipment according to the residual error value and the preset upper and lower limits of the residual error value.
In a third aspect, the present application discloses an electronic device comprising a processor and a memory; the processor implements the foregoing method for monitoring a device state when executing the computer program stored in the memory.
In a fourth aspect, the present application discloses a computer-readable storage medium for storing a computer program; wherein the computer program, when executed by a processor, implements the aforementioned device state monitoring method.
Therefore, the method comprises the steps of firstly obtaining sensor data detected by each sensor measuring point of the target equipment to obtain monitoring data to be evaluated, then setting elements in a pre-established influence degree matrix according to the influence degree of each sensor measuring point on the state of the target equipment to obtain a set influence degree matrix, calculating the distance between the monitoring data to be evaluated and historical monitoring data acquired in the normal running state of the target equipment by using the set influence degree matrix to obtain a distance value, calculating evaluation values corresponding to each sensor measuring point by using the distance value and the historical monitoring data, finally calculating residual errors between the evaluation values and the monitoring data to be evaluated to obtain residual values, and monitoring and evaluating the state of the target equipment according to the residual values and the upper limit and the lower limit of the preset residual values. According to the method and the device, the relative influence degree between the non-important sensor measuring points and the important sensor measuring points is adjusted through the adjustable influence degree matrix, so that the negative influence of the non-important sensor measuring points on the important sensor measuring points can be reduced, and a more accurate comprehensive equipment evaluation result is obtained.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only embodiments of the present application, and that other drawings may be obtained according to the provided drawings without inventive effort to a person skilled in the art.
FIG. 1 is a flow chart of a method for monitoring equipment status disclosed in the present application;
FIG. 2 is a flow chart of a specific device status monitoring method disclosed in the present application;
FIG. 3 is a schematic structural diagram of an apparatus for monitoring status of devices disclosed in the present application;
fig. 4 is a block diagram of an electronic device disclosed in the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all, of the embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
The embodiment of the application discloses a method for monitoring equipment state, which is shown in fig. 1 and comprises the following steps:
step S11: and acquiring sensor data detected by each sensor measuring point of the target equipment at present to obtain monitoring data to be evaluated.
In this embodiment, a plurality of sensor measurement points are preset to form a monitoring group for real-time monitoring of the target device, and sensor data of different sensor measurement points can be synchronously transmitted to the database without time delay. When the state monitoring needs to be performed on the target equipment, the sensor data detected by each current sensor measuring point can be collected first, so that a plurality of corresponding monitoring data to be subjected to state evaluation, namely the monitoring data to be evaluated, are obtained. The target equipment comprises, but is not limited to, a water pump, electronic equipment, a nuclear power station sensor and the like, and the sensor data detected by the sensor measuring point comprises, but is not limited to, multidimensional data such as temperature data, pressure data, current data, voltage data, medium flow data and the like.
Step S12: and setting elements in a pre-established influence degree matrix according to the influence degree of each sensor measuring point on the state of the target equipment, so as to obtain the set influence degree matrix.
In this embodiment, after obtaining to-be-evaluated monitoring data from sensor data detected by each sensor measurement point of a target device, further distinguishing the influence degree of each sensor measurement point on the state of the target device, and then performing corresponding setting on elements in a pre-created influence degree matrix according to the distinguishing result to obtain a set influence degree matrix. The influence degree matrix is n×n in size, and is an identity matrix during initialization, and the specific representation form is as follows:
wherein n is the total number of sensor measuring points, inf () is an influence matrix, influence adjustment of the influence matrix on the multidimensional sensor measuring points is firstly initialized according to the number of measuring points, the initialized matrix is a unit matrix, the row and column size of the matrix is equal to the total number n of the sensor measuring points, and when the corresponding influence degree of different sensor measuring points is required to be adjusted, only n elements corresponding to diagonal lines are required to be adjusted.
It can be understood that the importance of different positions and different types of sensor measuring points on the same equipment is different, for example, for a cooling water pump with complex characteristics, more measuring point distribution and more causes of abnormal states, the sensor data detected by the sensor measuring points mainly comprise physical quantities such as temperature, pressure, current, electric power and the like, wherein the correlation between the current value and the voltage value of the pump body and the operation condition and the operation state of the water pump is very great, the sensor measuring points are very important for evaluating the state of the water pump, so that accurate state monitoring is necessary, but the importance is relatively not high because the sensor data are uncontrollable variables for the sea water temperature or the atmospheric environment temperature of the inlet of the water pump. In this embodiment, elements in the pre-created influence degree matrix are set correspondingly according to different influence degrees, so that important sensor measurement points have higher priority than non-important sensor measurement points, and negative influence of the non-important sensor measurement points on the important sensor measurement points is reduced.
Specifically, the setting, according to the influence degree of each sensor measurement point on the state of the target device, the element in the pre-created influence degree matrix to obtain the set influence degree matrix may include: dividing the sensor measuring points according to the influence degree of each sensor measuring point on the state of the target equipment to obtain non-important sensor measuring points and important sensor measuring points; setting corresponding adjustment parameters for the non-important sensor measuring points and the important sensor measuring points according to the influence degree to obtain non-important sensor adjustment parameters and important sensor adjustment parameters; and setting elements in the pre-established influence degree matrix by using the non-important sensor adjustment parameters and the important sensor adjustment parameters to obtain the set influence degree matrix. In this embodiment, all sensor measurement points are divided according to different influence degrees of each sensor measurement point on a target device state to obtain an unimportant sensor measurement point and an important sensor measurement point, then corresponding adjustment parameters are set for the unimportant sensor measurement point and the important sensor measurement point according to the influence degrees respectively to obtain corresponding unimportant sensor adjustment parameters and important sensor adjustment parameters, and then elements in a pre-created influence degree matrix are set by using the unimportant sensor adjustment parameters and the important sensor adjustment parameters, so that a set influence degree matrix is obtained. For example, for cooling water pump equipment, there are n sensor measuring points altogether, wherein the sensor data corresponding to the unimportant sensor measuring points have the water pump inlet sea water temperature and the atmospheric environment temperature, the sensor data corresponding to the important sensor measuring points have the current value and the voltage value of the pump body, and during initialization, the n sensor measuring points have the same adjustment parameters, the adjustment parameters smaller than the current adjustment parameters can be set for the unimportant sensor data, the adjustment parameters larger than the current adjustment parameters can be set for the important sensor data, the current adjustment parameters can be kept for the sensor data with general importance, and then the elements in the pre-created influence degree matrix are set by utilizing the unimportant sensor adjustment parameters and the important sensor adjustment parameters.
Step S13: and calculating the distance between the monitoring data to be evaluated and the historical monitoring data collected under the normal running state of the target equipment by using the set influence matrix to obtain a distance value.
In this embodiment, elements in a pre-created influence degree matrix are set according to the influence degree of each sensor measurement point on the state of the target device, after the set influence degree matrix is obtained, the distance between the monitoring data to be evaluated and the historical monitoring data collected under the normal running state of the target device is calculated by using the set influence degree matrix, so as to obtain a corresponding distance value.
In this embodiment, the process of obtaining the history monitoring data may specifically include: collecting historical sensor data detected by each sensor measuring point in the normal running state of the target equipment to obtain historical sensor data; and processing the historical sensor data to obtain historical monitoring data. It should be noted that, in this embodiment, the historical monitoring data is specifically historical health data detected by each sensor measurement point in a normal operation state of the target device, and specifically, the historical sensor data detected by each sensor measurement point in the normal operation state of the target device may be collected first to obtain corresponding historical sensor data, and then the historical sensor data is processed to remove abnormal data and data in an abnormal state/under an abnormal working condition in the historical sensor data, so as to obtain effective historical monitoring data. Specifically, when the target device corresponds to n sensor measurement points, that is, n monitoring data, the historical monitoring data at the i time instant may be expressed as:
X(i)＝[X 1 (i)X 2 (i)X 3 (i)...X n (i)] T ；
further, in order to store the information contained in the history monitoring data, a storage matrix may be constructed, and in the process of constructing the storage matrix, the time sequence history monitoring data of the target device in the normal running state at the moment i is collected, where the data may include different working conditions, and if the total time sequence length is m, the storage matrix may be expressed as:
the storage matrix in the above formula can represent the normal operation process of the target device, that is, the whole normal operation space of the target device through each vector X.
Specifically, the calculation formula of the distance value may be expressed as:
that is, dis= [ Dis ] 1 dis 2 … dis m ] T ；
Where n is the total number of sensor points, inf () is the influence matrix, X obs Is the monitoring data to be estimated.
Specifically, the processing the historical sensor data to obtain historical monitoring data may include: normalizing the historical sensor data to obtain normalized data; and removing abnormal values in the normalized data to obtain historical monitoring data. In this embodiment, in order to ensure the quality of data, the historical sensor data detected by each sensor measurement point in the normal operation state of the target device may be normalized first, and then the abnormal values in the normalized data, such as the defect values and the abnormal value points, are deleted, where the deleted abnormal values are not only the abnormal deletion of the pure data, but also the deletion of the data in the abnormal state and the abnormal working condition, so that the used historical monitoring data is completely the data in the normal working condition and is not interfered by the abnormal working condition.
Step S14: and calculating the evaluation value corresponding to each sensor measuring point by using the distance value and the historical monitoring data.
In this embodiment, after calculating a distance value by using the set influence matrix to obtain a distance value between the monitored data to be evaluated and the historical monitored data collected in the normal operating state of the target device, calculating an evaluation value corresponding to each sensor measurement point by using the distance value and the historical monitored data.
In this embodiment, calculating the evaluation value corresponding to each sensor measurement point by using the distance value and the historical monitoring data may specifically include: and converting the distance value into a weight value, and calculating an evaluation value corresponding to the sensor measuring point by using the weight value and the historical monitoring data. The process of converting the distance value into the weight value can be realized by a gaussian kernel function. Specifically, the distance vector can be converted into the weight base number through gaussian kernel function transformation:
where h is the bandwidth of the kernel function, W is the weight of m vectors in the storage matrix, and then multiplying the weight by the storage matrix, and calculating an evaluation value according to the following formula:
step S15: and calculating residual errors between the evaluation value and the monitoring data to be evaluated to obtain residual values.
In this embodiment, after calculating the evaluation value corresponding to each sensor measurement point by using the distance value and the historical monitoring data, further, calculating the residual error between the evaluation value and the monitoring data to be evaluated to obtain a residual value. Specifically, the residual value between the actual value and the evaluation value can be expressed as:
res＝X est -X obs ；
wherein X is est To evaluate the value, X obs As the actual value, res is the residual value, rootThe residual value can realize the monitoring and evaluation of the multidimensional unsteady state.
Step S16: and monitoring and evaluating the state of the target equipment according to the residual value and the preset upper and lower limits of the residual value.
In this embodiment, after the residual value between the evaluation value and the monitored data to be evaluated is calculated, the state of the target device may be monitored and evaluated according to the upper and lower limits of the residual value, that is, the residual value is used as a basis for determining the state of the target device. For example, when the upper and lower limits of the preset residual value are a and b, respectively, and the target device is a cooling water pump, the judgment step S15 obtains whether the residual value res exceeds the upper limit a of the residual value, if so, an alarm with excessively high parameters is triggered, and if the residual value res is lower than the lower limit b (either positive or negative) of the residual value, an alarm with excessively low parameters is triggered. Upon the generation of an alarm, the state of the device changes from green to orange, indicating a transition of the device from a healthy state to an abnormal state. If the residual value res is between the upper and lower threshold values a and b, that is, does not exceed the upper and lower threshold value ranges, no alarm is generated, and the state of the equipment is green, which means that the current equipment is in a healthy state without faults, thereby realizing the detection and evaluation of the healthy state of the equipment.
It can be seen that, in the embodiment of the present application, sensor data detected by each sensor measurement point of a target device is obtained to obtain monitored data to be evaluated, then elements in a pre-created influence degree matrix are set according to influence degrees of each sensor measurement point on a state of the target device to obtain a set influence degree matrix, then a distance between the monitored data to be evaluated and historical monitored data collected in a normal running state of the target device is calculated by using the set influence degree matrix to obtain a distance value, evaluation values corresponding to each sensor measurement point are calculated by using the distance value and the historical monitored data, residual values are obtained by calculating residual errors between the evaluation values and the monitored data to be evaluated, and states of the target device are monitored and evaluated according to the residual values and preset upper and lower limits of the residual values. According to the embodiment of the application, the relative influence degree between the non-important sensor measuring points and the important sensor measuring points is adjusted by using the adjustable influence degree matrix, so that the negative influence of the non-important sensor measuring points on the important sensor measuring points can be reduced, and a more accurate comprehensive equipment evaluation result is obtained.
The embodiment of the application discloses a specific equipment state monitoring method, which is shown in fig. 2 and comprises the following steps:
step S21: and acquiring sensor data detected by each sensor measuring point of the target equipment at present to obtain monitoring data to be evaluated.
Step S22: and dividing the sensor measuring points according to the influence degree of each sensor measuring point on the state of the target equipment to obtain non-important sensor measuring points and important sensor measuring points.
Step S23: and setting corresponding adjustment parameters for the non-important sensor measuring points and the important sensor measuring points according to the influence degree to obtain non-important sensor adjustment parameters and important sensor adjustment parameters.
Step S24: and reducing elements corresponding to the non-important sensor measuring points in the pre-created influence degree matrix by using the non-important sensor adjusting parameters to obtain a set influence degree matrix.
In this embodiment, after the non-important sensor adjustment parameters and the important sensor adjustment parameters are obtained by setting the corresponding adjustment parameters for the non-important sensor measurement points and the important sensor measurement points according to the influence degrees, in order to reduce the negative influence of the non-important sensor measurement points on the important sensor measurement points, the elements corresponding to the non-important sensor measurement points in the influence degree matrix created in advance may be reduced by using the non-important sensor adjustment parameters. For example, the influence degree of the non-important sensor measurement point on other sensor measurement points is reduced or closed, if the non-important sensor measurement point is the kth sensor measurement point in the n sensor measurement points, the element values of the kth row and the kth column in the influence degree matrix can be set to 0.1 or 0, so that the influence of the non-important sensor measurement point is reduced.
Step S25: and adjusting elements corresponding to the important sensor measuring points in the influence degree matrix by using the important sensor adjusting parameters to obtain the set influence degree matrix.
Further, in order to obtain a more accurate evaluation result of the sensor measurement point and make the influence of other sensor measurement points smaller, the element corresponding to the important sensor measurement point in the influence degree matrix may be adjusted by using the important sensor adjustment parameter. For example, when the influence degree of a single important sensor measuring point is adjusted, the element values of the kth row and the kth column of the influence degree matrix corresponding to the important sensor measuring point can be increased by one order of magnitude to 10, and the specific order of magnitude of increase can be set according to actual needs, such as n×10.
Furthermore, for the sensor measuring points of a certain type (more than two), the influence degree of the sensor measuring points can be improved together, so that the accuracy of the evaluation result of the sensor measuring points of the certain type is improved, and the influence of the sensor measuring points of other types is reduced. For example, when multiple important sensor measurement points with the same important sensor adjustment parameters need to be adjusted, the corresponding kth 1, k2,..once, kp sensor measurement points may be adjusted in batches, such as each measurement point being adjusted to (n×10)/p.
Step S26: and calculating the distance between the monitoring data to be evaluated and the historical monitoring data collected under the normal running state of the target equipment by using the set influence matrix to obtain a distance value.
Step S27: and converting the distance value into a weight value by using a Gaussian kernel function, and calculating an evaluation value corresponding to the sensor measuring point by using the weight value and the historical monitoring data.
Step S28: and calculating residual errors between the evaluation value and the monitoring data to be evaluated to obtain residual values.
Step S29: and monitoring and evaluating the state of the target equipment according to the residual value and the preset upper and lower limits of the residual value.
For more specific processing procedures of the steps S21, S22, S23, S26, S27, S28, and S29, reference may be made to the corresponding contents disclosed in the foregoing embodiments, and no further description is given here.
Therefore, the embodiment of the application divides the sensor measuring points according to the influence degree, sets corresponding adjustment parameters for the divided non-important sensor measuring points and the important sensor measuring points respectively, then adjusts down elements corresponding to the non-important sensor measuring points in the pre-established influence degree matrix by using the non-important sensor adjustment parameters, adjusts up elements corresponding to the important sensor measuring points in the influence degree matrix by using the important sensor adjustment parameters, thereby reducing the negative influence of the non-important sensor measuring points on the important sensor measuring points, enabling the important sensor measuring points to have higher priority than the non-important sensor measuring points in state monitoring, and further improving the precision of the comprehensive evaluation result of the equipment.
Correspondingly, the embodiment of the application also discloses a device for monitoring the equipment state, as shown in fig. 3, the device comprises:
the data acquisition module 11 is used for acquiring sensor data detected by each sensor measuring point of the target equipment currently to obtain monitoring data to be evaluated;
the element setting module 12 is configured to set elements in a pre-created influence degree matrix according to the influence degree of each sensor measurement point on the state of the target device, so as to obtain a set influence degree matrix;
the distance calculation module 13 is configured to calculate a distance between the monitored data to be evaluated and the historical monitored data collected in the normal operation state of the target device by using the set influence matrix, so as to obtain a distance value;
an evaluation value calculating module 14, configured to calculate an evaluation value corresponding to each sensor measurement point by using the distance value and the historical monitoring data;
the residual calculation module 15 is used for calculating residual between the evaluation value and the monitoring data to be evaluated to obtain a residual value;
and the monitoring and evaluating module 16 is used for monitoring and evaluating the state of the target equipment according to the residual value and the preset upper and lower limits of the residual value.
The specific workflow of each module may refer to the corresponding content disclosed in the foregoing embodiment, and will not be described herein.
It can be seen that, in this embodiment of the present application, sensor data detected by each sensor measurement point of a target device is obtained first to obtain monitored data to be evaluated, then elements in a pre-created influence degree matrix are set according to the influence degree of each sensor measurement point on the state of the target device to obtain a set influence degree matrix, then a distance between the monitored data to be evaluated and historical monitored data collected in a normal operation state of the target device is calculated by using the set influence degree matrix to obtain a distance value, then evaluation values corresponding to each sensor measurement point are calculated by using the distance value and the historical monitored data, residual values obtained by calculating residual errors between the evaluation values and the monitored data to be evaluated are finally calculated, and the state of the target device is monitored and evaluated according to the residual values and upper and lower limits of preset residual values. According to the embodiment of the application, the relative influence degree between the non-important sensor measuring points and the important sensor measuring points is adjusted by using the adjustable influence degree matrix, so that the negative influence of the non-important sensor measuring points on the important sensor measuring points can be reduced, and a more accurate comprehensive equipment evaluation result is obtained.
In some specific embodiments, the evaluation value calculating module 14 may specifically include:
the first weight conversion unit is used for converting the distance value into a weight value;
and the evaluation value calculation unit is used for calculating the evaluation value corresponding to the sensor measuring point by using the weight value and the historical monitoring data.
In some specific embodiments, the first weight conversion unit may specifically include:
and the second weight conversion unit is used for converting the distance value into a weight value by using a Gaussian kernel function.
In some specific embodiments, the device state monitoring apparatus may further include:
the historical data acquisition unit is used for acquiring historical sensor data detected by each sensor measuring point in the normal running state of the target equipment to obtain historical sensor data;
and the historical data processing unit is used for processing the historical sensor data to obtain historical monitoring data.
In some specific embodiments, the historical data processing unit may specifically include:
the data normalization unit is used for performing normalization processing on the historical sensor data to obtain normalized data;
and the abnormal value removing unit is used for removing abnormal values in the normalized data to obtain historical monitoring data.
In some specific embodiments, the element setting module 12 may specifically include:
the sensor measuring point carrying out unit is used for dividing the sensor measuring points according to the influence degree of each sensor measuring point on the state of the target equipment to obtain non-important sensor measuring points and important sensor measuring points;
the adjusting parameter setting unit is used for setting corresponding adjusting parameters for the non-important sensor measuring point and the important sensor measuring point according to the influence degree to obtain non-important sensor adjusting parameters and important sensor adjusting parameters;
and the element setting unit is used for setting elements in the pre-created influence degree matrix by using the non-important sensor adjustment parameters and the important sensor adjustment parameters to obtain a set influence degree matrix.
In some specific embodiments, the element setting module 12 may specifically include:
the element reduction unit is used for reducing elements corresponding to the non-important sensor measuring points in the pre-created influence degree matrix by utilizing the non-important sensor adjustment parameters to obtain a set influence degree matrix;
and/or an element enlarging unit, configured to enlarge an element corresponding to the important sensor measurement point in the influence matrix by using the important sensor adjustment parameter, so as to obtain a set influence matrix.
Further, the embodiment of the present application further discloses an electronic device, and fig. 4 is a block diagram of an electronic device 20 according to an exemplary embodiment, where the content of the figure is not to be considered as any limitation on the scope of use of the present application.
Fig. 4 is a schematic structural diagram of an electronic device 20 according to an embodiment of the present application. The electronic device 20 may specifically include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input output interface 25, and a communication bus 26. Wherein the memory 22 is configured to store a computer program that is loaded and executed by the processor 21 to implement the relevant steps in the device status monitoring method disclosed in any of the foregoing embodiments. In addition, the electronic device 20 in the present embodiment may be specifically an electronic computer.
In this embodiment, the power supply 23 is configured to provide an operating voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and an external device, and the communication protocol to be followed is any communication protocol applicable to the technical solution of the present application, which is not specifically limited herein; the input/output interface 25 is used for acquiring external input data or outputting external output data, and the specific interface type thereof may be selected according to the specific application requirement, which is not limited herein.
The memory 22 may be a carrier for storing resources, such as a read-only memory, a random access memory, a magnetic disk, or an optical disk, and the resources stored thereon may include an operating system 221, a computer program 222, and the like, and the storage may be temporary storage or permanent storage.
The operating system 221 is used for managing and controlling various hardware devices on the electronic device 20 and computer programs 222, which may be Windows Server, netware, unix, linux, etc. The computer program 222 may further include a computer program that can be used to perform other specific tasks in addition to the computer program that can be used to perform the device status monitoring method performed by the electronic device 20 disclosed in any of the previous embodiments.
Further, the application also discloses a computer readable storage medium for storing a computer program; wherein the computer program, when executed by a processor, implements the device state monitoring method disclosed previously. For specific steps of the method, reference may be made to the corresponding contents disclosed in the foregoing embodiments, and no further description is given here.
In this specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, so that the same or similar parts between the embodiments are referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative elements and steps are described above generally in terms of functionality in order to clearly illustrate the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. The software modules may be disposed in Random Access Memory (RAM), memory, read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
Finally, it is further noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The foregoing has described in detail a method, apparatus, device and storage medium for monitoring a device state, where specific examples are applied to illustrate the principles and embodiments of the present application, and the above description of the examples is only for helping to understand the method and core idea of the present application; meanwhile, as those skilled in the art will have modifications in the specific embodiments and application scope in accordance with the ideas of the present application, the present description should not be construed as limiting the present application in view of the above.
Claims (10)
1. A method for monitoring a status of a device, comprising:
acquiring sensor data detected by each sensor measuring point of the target equipment at present to obtain monitoring data to be evaluated;
setting elements in a pre-established influence degree matrix according to the influence degree of each sensor measuring point on the state of the target equipment to obtain a set influence degree matrix;
calculating the distance between the monitoring data to be evaluated and the historical monitoring data collected under the normal running state of the target equipment by using the set influence matrix to obtain a distance value;
calculating an evaluation value corresponding to each sensor measuring point by using the distance value and the historical monitoring data;
calculating residual errors between the evaluation value and the monitoring data to be evaluated to obtain residual values;
and monitoring and evaluating the state of the target equipment according to the residual value and the preset upper and lower limits of the residual value.
2. The apparatus state monitoring method according to claim 1, wherein the calculating an evaluation value corresponding to each sensor measurement point using the distance value and the history monitoring data comprises:
and converting the distance value into a weight value, and calculating an evaluation value corresponding to the sensor measuring point by using the weight value and the historical monitoring data.
3. The device status monitoring method of claim 2, wherein the converting the distance value into a weight value comprises:
the distance value is converted into a weight value using a gaussian kernel function.
4. The device status monitoring method of claim 1, further comprising:
collecting historical sensor data detected by each sensor measuring point in the normal running state of the target equipment to obtain historical sensor data;
and processing the historical sensor data to obtain historical monitoring data.
5. The method of claim 4, wherein processing the historical sensor data to obtain historical monitoring data comprises:
normalizing the historical sensor data to obtain normalized data;
and removing abnormal values in the normalized data to obtain historical monitoring data.
6. The device state monitoring method according to any one of claims 1 to 5, wherein the setting the elements in the pre-created influence matrix according to the influence degree of each sensor measurement point on the target device state, to obtain the set influence matrix, includes:
dividing the sensor measuring points according to the influence degree of each sensor measuring point on the state of the target equipment to obtain non-important sensor measuring points and important sensor measuring points;
setting corresponding adjustment parameters for the non-important sensor measuring points and the important sensor measuring points according to the influence degree to obtain non-important sensor adjustment parameters and important sensor adjustment parameters;
and setting elements in the pre-established influence degree matrix by using the non-important sensor adjustment parameters and the important sensor adjustment parameters to obtain the set influence degree matrix.
7. The method for monitoring a device state according to claim 1, wherein the setting elements in a pre-created influence matrix by using the non-important sensor adjustment parameters and the important sensor adjustment parameters to obtain the set influence matrix comprises:
reducing elements corresponding to the non-important sensor measuring points in a pre-created influence degree matrix by using the non-important sensor adjusting parameters to obtain a set influence degree matrix;
and/or, using the important sensor adjustment parameters to adjust elements corresponding to the important sensor measuring points in the influence degree matrix, so as to obtain the set influence degree matrix.
8. A device state monitoring apparatus, comprising:
the data acquisition module is used for acquiring sensor data detected by each sensor measuring point of the target equipment at present to obtain monitoring data to be evaluated;
the element setting module is used for setting elements in a pre-established influence degree matrix according to the influence degree of each sensor measuring point on the state of the target equipment to obtain a set influence degree matrix;
the distance calculation module is used for calculating the distance between the monitoring data to be evaluated and the historical monitoring data collected under the normal running state of the target equipment by using the set influence matrix to obtain a distance value;
the evaluation value calculation module is used for calculating the evaluation value corresponding to each sensor measuring point by using the distance value and the historical monitoring data;
the residual calculation module is used for calculating residual errors between the evaluation value and the monitoring data to be evaluated to obtain a residual value;
and the monitoring and evaluating module is used for monitoring and evaluating the state of the target equipment according to the residual error value and the preset upper and lower limits of the residual error value.
9. An electronic device comprising a processor and a memory; wherein the processor, when executing the computer program stored in the memory, implements the device state monitoring method according to any one of claims 1 to 7.
10. A computer-readable storage medium storing a computer program; wherein the computer program, when executed by a processor, implements the device status monitoring method of any one of claims 1 to 7.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116302848A (en) * | 2023-05-19 | 2023-06-23 | 杭州安脉盛智能技术有限公司 | Detection method and device for bias of evaluation value, electronic equipment and medium |
| CN116595013A (en) * | 2023-07-17 | 2023-08-15 | 中国水利水电第七工程局有限公司 | Multi-type monitoring information storage and calculation method and system |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116302848A (en) * | 2023-05-19 | 2023-06-23 | 杭州安脉盛智能技术有限公司 | Detection method and device for bias of evaluation value, electronic equipment and medium |
| CN116302848B (en) * | 2023-05-19 | 2023-09-01 | 杭州安脉盛智能技术有限公司 | Detection method and device for bias of evaluation value, electronic equipment and medium |
| CN116595013A (en) * | 2023-07-17 | 2023-08-15 | 中国水利水电第七工程局有限公司 | Multi-type monitoring information storage and calculation method and system |
| CN116595013B (en) * | 2023-07-17 | 2023-12-01 | 中国水利水电第七工程局有限公司 | Multi-type monitoring information storage and calculation method and system |
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