CN115307670A - Method, apparatus and medium for locating central air conditioning system anomaly sensors - Google Patents
Method, apparatus and medium for locating central air conditioning system anomaly sensors Download PDFInfo
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Abstract
Embodiments of the present disclosure relate to a method for locating an anomaly sensor in a system under test, comprising: acquiring a first detection model, a second detection model and a corresponding statistic threshold; acquiring a plurality of groups of data to be detected from a plurality of sensors of a system to be detected, and analyzing the data to be detected by using a first detection model and a second detection model so as to determine a statistic value of the data to be detected corresponding to the first detection model and the second detection model and a residual value corresponding to the second model; determining a first failure probability and a second failure probability of each sensor of the plurality of sensors corresponding to the first detection model and the second detection model based on the statistic threshold value and the statistic value; determining a third failure probability of a portion of the plurality of sensors based on the second failure probability and a residual value corresponding to the second detection model; and determining the fourth fault probability of a part of the sensors in the plurality of sensors based on the constraint condition of the operation of the system to be tested, thereby positioning the abnormal sensor of the system to be tested.
Description
Technical Field
Embodiments of the present disclosure relate generally to the field of fault diagnosis, and more particularly, to a method, apparatus, and medium for locating an abnormal sensor.
Background
With the development of intelligence, sensors play an increasingly important role. In various systems (e.g. air conditioning systems), the measurement signal of a sensor (e.g. of an end wind cabinet in an air conditioning system) is the basis for controlling and monitoring the system. Therefore, if the data provided by the sensors is unreliable or inaccurate, it may cause a decision deviation of the control strategy, which in turn may result in an increase in energy consumption of the whole system or a decrease in environmental comfort. In general, the sensor has a problem of measurement deviation after a long-term use, and it is very important to timely detect such abnormality or malfunction of the sensor.
Most of current methods for sensor fault detection research are data-driven methods, and a sensor diagnosis method based on Principal Component Analysis (PCA) is widely researched. The principal component analysis method mainly comprises the steps of utilizing high correlation among variables to project data to be detected to a principal component subspace and a residual error subspace respectively, when a fault occurs, the projection in the residual error subspace is increased remarkably, and whether the fault occurs is detected by constructing statistics and analyzing the projection through a threshold value of the statistics.
The method has strong sensitivity to abnormal data in the sensor and high detection efficiency, but has certain limitation when being practically applied to the field of central air conditioners. Firstly, the flow rate of chilled water and the circulating air volume as key input parameters are difficult to obtain; next, after detecting the sensor abnormality, a certain misjudgment rate exists when specifically determining which sensor is abnormal, especially when a plurality of sensors are failed concurrently. This is because the principal component analysis method is a black box model and does not involve physical relationships between system parameters. Therefore, a problem in the prior art for locating an abnormal sensor is that the abnormal sensor in the system cannot be accurately located when the system has a plurality of sensors.
Disclosure of Invention
In view of the above, the present disclosure provides a method and apparatus for locating an abnormal sensor, enabling an expert rule-based decoupling method to improve the ability of principal component analysis sensor fault diagnosis. The method is based on a principal component analysis method and combines professional rules, and a rule model is added for optimization by analyzing the physical relation among variables during data cleaning, model training and fault diagnosis, so that the quality of training data, the accuracy of a detection model and the diagnosis efficiency of an abnormal sensor are effectively improved. The method is suitable for various air conditioner tail end air cabinet systems, and can complete fault detection of the sensor on line. According to a first aspect of the present disclosure, there is provided a method for locating an abnormal sensor in a system under test, comprising: acquiring a first detection model, a second detection model and a corresponding statistic threshold; acquiring a plurality of groups of data to be detected from a plurality of sensors of a system to be detected, and analyzing the data to be detected by using a first detection model and a second detection model so as to determine a statistic value of the data to be detected corresponding to the first detection model and the second detection model and a residual value corresponding to the second model; determining a first failure probability and a second failure probability for each sensor of the plurality of sensors corresponding to the first detection model and the second detection model based on the statistic threshold value and the statistic value; determining a third probability of failure for a portion of the plurality of sensors based on the second probability of failure and the residual values corresponding to the second detection model; and determining a fourth fault probability of a part of sensors in the plurality of sensors based on a constraint condition of the operation of the system to be tested, so as to locate the abnormal sensor of the system to be tested based on the first fault probability, the second fault probability, the third fault probability and the fourth fault probability.
According to a second aspect of the present disclosure, there is provided a computing device comprising: at least one processor; and a memory communicatively coupled to the at least one processor; the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of the first aspect of the disclosure.
In a third aspect of the present disclosure, a non-transitory computer readable storage medium is provided having stored thereon computer instructions for causing a computer to perform the method of the first aspect of the present disclosure.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.
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The above and other features, advantages and aspects of various embodiments of the present disclosure will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. In the drawings, like or similar reference characters designate like or similar elements.
FIG. 1 shows a schematic diagram of an example system 100 for implementing a method for locating an anomaly sensor according to an embodiment of the present invention.
Fig. 2 shows a schematic diagram of an example sensor arrangement 200, in accordance with embodiments of the present disclosure.
FIG. 3 shows a flow diagram of a method 300 for locating an anomalous sensor in a system under test in accordance with an embodiment of the disclosure.
FIG. 4 shows a flow diagram of a method 400 of constructing a detection model for locating an anomalous sensor in a system under test in accordance with an embodiment of the present disclosure.
FIG. 5 shows a second detection model according to an embodiment of the present invention in relation to the difference between the inlet and outlet air temperature differences and the difference between the heat exchanging ends of the surface air cooler.
Fig. 6 shows a block diagram of an electronic device 600 according to an embodiment of the disclosure.
Detailed Description
Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the disclosure are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
The term "include" and variations thereof as used herein is meant to be inclusive in an open-ended manner, i.e., "including but not limited to". Unless specifically stated otherwise, the term "or" means "and/or". The term "based on" means "based at least in part on". The terms "one example embodiment" and "one embodiment" mean "at least one example embodiment". The term "another embodiment" means "at least one additional embodiment". The terms "first," "second," and the like may refer to different or the same object. Other explicit and implicit definitions are also possible below.
As described above, most of current methods of sensor failure detection research are data-driven based methods, and among them, a sensor diagnosis method based on Principal Component Analysis (PCA) is widely studied. The principal component analysis method mainly utilizes the high correlation among variables to project data to be detected to a principal component subspace and a residual error subspace respectively, when a fault occurs, the projection in the residual error subspace is increased remarkably, and the projection is analyzed through the construction statistics and the threshold value thereof to detect whether the fault occurs. The method has strong sensitivity to abnormal data in the sensor and high detection efficiency, but has certain limitation when being practically applied to the field of central air conditioners. Firstly, the flow rate of chilled water and the circulating air volume as key input parameters are difficult to obtain; next, after detecting the sensor abnormality, a certain misjudgment rate exists when specifically locating which sensor is abnormal, especially when a plurality of sensors are failed concurrently. This is because the principal component analysis method is a black box model and does not involve physical relationships between system parameters. Therefore, a problem in the prior art for locating an abnormal sensor is that the abnormal sensor in the system cannot be accurately located when there are a plurality of sensors or sensor groups in the system.
To address at least in part one or more of the above problems and other potential problems, an example embodiment of the present disclosure is directed to a method for locating an anomalous sensor in a system under test, comprising: acquiring a first detection model, a second detection model and a corresponding statistic threshold; acquiring a plurality of groups of data to be detected from a plurality of sensors of a system to be detected, and analyzing the data to be detected by using a first detection model and a second detection model so as to determine statistical values of the data to be detected corresponding to the first detection model and the second detection model and residual values corresponding to the second model; determining a first failure probability and a second failure probability for each sensor of the plurality of sensors corresponding to the first detection model and the second detection model based on the statistic threshold value and the statistic value; determining a third probability of failure for a portion of the plurality of sensors based on the second probability of failure and the residual values corresponding to the second detection model; and determining a fourth fault probability of a part of sensors in the plurality of sensors based on the constraint condition of the operation of the system to be tested, so as to locate the abnormal sensor of the system to be tested based on the first fault probability, the second fault probability, the third fault probability and the fourth fault probability.
In this way, the on-line diagnosis of the sensor included in the tested equipment can be completed by utilizing the real-time operation parameters of the tested equipment, so that the sensor with faults is accurately detected in the tested system including a plurality of sensors, the accuracy and the reliability of the data measured by the sensors are further ensured, and effective data support is provided for the safe operation and energy-saving control strategy of the tested equipment. Moreover, the method can be used in various tested devices with corresponding sensors, so that the mobility and the universality are high.
FIG. 1 shows a schematic diagram of an example system 100 for implementing a method for locating an anomaly sensor according to an embodiment of the invention. As shown in fig. 1, the system 100 includes one or more fault diagnostic devices 110 and a system under test 120. The fault diagnosis device 110 and the system under test 120 may perform data interaction via a host communication protocol, for example, over the network 130. In the present disclosure, the system under test 120 may be a system including a plurality of sensors 1201, such as an end wind cabinet in a central air conditioning system. The fault diagnosis apparatus 110 may be used to perform fault diagnosis on the system under test 120, including performing fault diagnosis on a plurality of sensors 1201 included in the system under test 120 to detect faulty (or abnormal) sensors among the sensors. The fault diagnosis device 110 may be implemented by a computing device such as a desktop, laptop, notebook, industrial control computer, or cloud platform, which may include at least one processor 1101 and at least one memory 1102 coupled to the at least one processor 1101, the memory 1102 having stored therein instructions executable by the at least one processor 1102 which, when executed by the at least one processor 1101, perform the methods 300 and 400 as described below. The specific structure of the fault diagnosis device 110 may be, for example, the electronic device 600 described below in conjunction with fig. 6. The system under test 120 may be, for example, an end wind cabinet in a central air conditioning system.
Fig. 2 shows a schematic diagram of an example sensor arrangement 200, in accordance with embodiments of the present disclosure. As shown in fig. 2, the sensor arrangement 200 may comprise a surface cooler 201, a fresh air sensor S OA Wind mixing sensor S MA Air return sensor S RA Air supply sensor S SA Chilled water supply sensor S WATER Fresh air valve opening sensor V OA . It is noted that the sensor arrangement 200 may also comprise other sensors such as temperature sensors, humidity sensors, opening sensors or power meters. These sensors for use in air can all be used to measure temperature as well as humidity simultaneously. For example, the fresh air sensor S OA Can be used for measuring fresh air temperature T OA And fresh air humidity R OA Wind mixing sensor S MA Can be used for measuring the temperature T of mixed air MA And mixed air humidity R MA Return air sensor S RA Can be used for measuring the return air temperature T RA And return air humidity R RA Air supply sensor S SA Can be used for measuring the temperature T of the air supply SA And the supply air humidity R SA Chilled water supply sensor S WATER Can be used for supplying water temperature T for chilled water WATER . To the opening degree V of the fresh air valve OA Opening degree V of water valve WATER Opening degree V of bypass valve BYPASS The parameters may be directly output by the system under test 120, or obtained through analog calculation based on the output of the system under test 120, which is not described herein again. The solution of the present disclosure can be used for detecting an abnormal (or malfunctioning) sensor among these sensors. The system under test according to the present invention may be, for example, a central air conditioning system.
FIG. 3 shows a flow chart of a method 300 for locating an anomalous sensor in a system under test in accordance with an embodiment of the present disclosure. The method 300 may be performed by the fault diagnosis device 110 shown in fig. 1, and the specific structure of the fault diagnosis device 110 may be shown as the electronic device 600 shown in fig. 6. It should be understood that method 300 may also include additional blocks not shown and/or may omit blocks shown, as the scope of the disclosure is not limited in this respect.
In step 302, the fault diagnosis device 110 may obtain the first detection model and the second detection model and the corresponding statistic thresholds.
In one embodiment, the first detection model may be an energy conservation model and the second detection model may be a heat exchange model and a statistic threshold Q of the corresponding first detection model 10 And a statistic threshold Q of the corresponding second detection model 20 。
Specifically, an energy conservation model (i.e., a first detection model) and a heat exchange model (i.e., a second detection model) are constructed via the steps of the method 400.
FIG. 4 shows a flow diagram of a method 400 of constructing a detection model for locating an anomalous sensor in a system under test in accordance with an embodiment of the present disclosure. The method 400 may be performed by the fault diagnosis device 110 shown in fig. 1, and the specific structure of the fault diagnosis device 110 may be shown as the electronic device 600 shown in fig. 6. It should be understood that method 400 may also include additional blocks not shown and/or may omit blocks shown, as the scope of the disclosure is not limited in this respect.
At step 402, the fault diagnosis device 110 may acquire sets of first operating parameters from a plurality of sensors of the system under test. The sensors can comprise a fresh air sensor, a return air sensor, a mixed air sensor, an air supply sensor, a chilled water supply temperature sensor and the like. The first operation parameters measured by the sensors comprise fresh air temperature, fresh air humidity, return air temperature, return air humidity, mixed air temperature, mixed air humidity, fresh air valve opening, fan power, air supply temperature, air supply humidity, chilled water supply temperature, water pump power, water valve opening and bypass valve opening of the system to be measured.
The obtaining of the plurality of sets of first operating parameters may be a plurality of sets of parameters acquired in real time based on a time interval within an acquisition time period. For example, a set of parameters (fresh air temperature, fresh air humidity, return air temperature, return air humidity, mixed air temperature, mixed air humidity, fresh air valve opening, fan power, supply air temperature, supply air humidity, chilled water supply water temperature, water pump power, water valve opening, bypass valve opening) is collected at time intervals of every 10 minutes in a collection time period of 24 hours. In this manner, the fault diagnosis device 110 may acquire 144 sets of the first operating parameters. It is noted that the acquisition time period and time interval may be dynamically adjusted according to the requirements of the system under test.
At step 404, the fault diagnosis device 110 may perform a screening on the acquired plurality of sets of first operating parameters based on the constraints of the operation of the system under test, thereby acquiring screened first operating parameters. The constraints may include the following conditions: the air mixing temperature of the tested system is between the fresh air temperature and the return air temperature of the tested system; the air mixing humidity of the tested system is between the fresh air humidity and the return air humidity of the tested system; the air supply temperature of the system to be tested is lower than the air mixing temperature of the system to be tested and higher than the chilled water supply temperature of the system to be tested; and the air supply humidity of the tested system is lower than the air mixing humidity of the tested system. The screened first operating parameter may be stable operating data of the system under test. The first operating parameter may include fan power
Fresh air temperature
Fresh air humidity
Temperature of return air
Humidity of return air
Temperature of mixed air
Humidity of mixed air
Temperature of the air supply
Humidity of air supply
Power of the chilled water pump
Supply temperature of chilled water
Such as the opening of a fresh air valve
Opening degree of water valve
Opening degree of the bypass valve
Electrical signals of (a) and the like.
In step 406, the fault diagnosis device 110 may determine a second operating parameter based on the filtered first operating parameter. Specifically, temperature difference of inlet and outlet air
Can be controlled by the temperature of mixed air
And temperature of the supply air
The calculation specifically comprises the following steps:
heat exchange end difference of surface cooler
Can be controlled by the temperature of the supplied air
And chilled water supply temperature
The calculation specifically comprises the following steps:
thus, the second operating parameter may include an inlet and outlet air temperature difference determined based on the temperature of the mixed air and the temperature of the supply air, and a surface cooler heat exchange end difference determined based on the temperature of the supply air and the temperature of the chilled water supply.
In step 408, the fault diagnosis device 110 may establish a first data matrix from the filtered first operating parameters and a second data matrix from the filtered first and second operating parameters.
The first data matrix can be established by the first operation parameter through the screening, and the first operation parameter through the screening specifically includes new trend temperature, new trend humidity, return air temperature, return air humidity, mixed wind temperature, mixed wind humidity, new trend valve opening degree, fan power open the third power.
The second data matrix can be established by the screened first operation parameters and the screened second operation parameters, and the operation parameters specifically comprise mixed air temperature, mixed air humidity, air supply temperature, air supply humidity, chilled water supply temperature, fan power open cubic, water pump power open cubic, air inlet and outlet temperature difference, surface cooler heat exchange end difference, water valve opening degree and bypass valve opening degree.
In step 410, the fault diagnosis device 110 may determine principal component vectors, residual vectors, and statistic thresholds corresponding to the first data matrix and the second data matrix, thereby obtaining a first detection model and a second detection model.
The fault diagnosis device 110 may calculate eigenvalues and eigenvectors of the first data matrix and the second data matrix after establishing the first data matrix for the first detection model and the second data matrix for the second detection model and normalizing the operating parameters of the first data matrix and the second data matrix.
The fault diagnosis device 110 may determine the number of principal elements of the first data matrix and the second data matrix by a principal element contribution percentage method (e.g., determining the principal element contribution rate to be 85%). Determining principal component vectors and residual vectors of the first data matrix and the second data matrix and a statistic threshold Q representing normal data according to the determined number of principal components 0 And waiting for key detection parameters so as to obtain an energy conservation model (namely, a first detection model) and a heat exchange model (namely, a second detection model).
In step 304, the fault diagnosis device 110 may obtain multiple sets of data to be detected from multiple sensors of the system under test, and analyze the data to be detected using the first detection model and the second detection model, so as to determine statistical values of the data to be detected corresponding to the first detection model and the second detection model and residual values corresponding to the second model.
In one embodiment, the fault diagnosis device 110 may be derived from the fresh air sensor S of the system under test OA Wind mixing sensor S MA Air return sensor S RA Air supply sensor S SA Chilled water supply sensor S WATER The sensors acquire multiple sets of data to be detected and the data to be detected is analyzed using the first detection model and the second detection model determined in method 400. Specifically, the fault diagnosing apparatus 110 may acquire a first matrix and a second matrix corresponding to a first detection model and a second detection model. Based on the first matrix and the second matrix, the fault diagnosis device 110 may apply the data matrix of the data to be detected corresponding to the first detection model and the data matrix of the data to be detected corresponding to the second detection model to the principal component vector and the residual vector of the first detection model and the principal component vector and the residual vector of the second detection model, respectivelyThe decomposition is carried out quantitatively. And determining statistic values of the data to be detected corresponding to the first detection model and the second detection model and residual values corresponding to the second model by calculating statistic Q of any group of data to be detected. The failure diagnosing apparatus 110 may also calculate the contribution rate of each sensor involved in the first detection model and the second detection model to the statistic Q and the residual value of each sensor parameter.
In step 306, the fault diagnosis device 110 may determine a first fault probability and a second fault probability for each sensor of the plurality of sensors corresponding to the first detection model and the second detection model based on the statistical quantity threshold value and the statistical quantity value.
In one embodiment, the fault diagnosis device 110 may define a parameter, total fault probability F, for each sensor (which may measure temperature and humidity) in the system under test, with an initial value of zero. The fault diagnosis device 110 may compare the statistics Q of each set of operating parameters of the data to be detected acquired in step 304 with the threshold Q of the corresponding detection model 0 And comparing to determine whether the sensor corresponding to the operation parameter has an abnormality. It should be noted that, detecting that the model exceeds the threshold or the residual exceeds the threshold means that the number of sets of the operation parameter data satisfying the condition in the model data matrix exceeds a preset percentage (for example, fifty percent).
Thus, for each sensor of the plurality of sensors, the sensor is free of anomalies if it does not exceed the threshold at both the first detection model and the second detection model. If the first detection model exceeds the threshold value, assigning the contribution rate of the statistic Q of the sensors in the first detection model to the first fault probability F of the corresponding sensors 1 . Assigning the contribution rate of the statistics Q of the sensors in the second detection model to a second failure probability F of the corresponding sensor if the second detection model exceeds the threshold value 2 . Repeating the above operations for each of the plurality of sensors to determine a first failure probability F for each of the plurality of sensors corresponding to the first and second detection models 1 And a second probability of failure F 2 。
In step 308, the fault diagnosis device 110 may determine a third probability of fault for a portion of the plurality of sensors based on the second probability of fault and the residual values corresponding to the second detection model.
In one embodiment, if the second detection model exceeds the threshold, the fault diagnosis device 110 may assign a contribution rate of the statistics Q of the sensors in the second detection model to the second fault probability F of the corresponding sensors 2 And at the same time re-assigned according to the residual relation of the operating parameters shown in fig. 5.
Fig. 5 shows a relationship between the second detection model and the difference between the inlet and outlet air temperature difference values and the difference between the heat exchange ends of the surface air coolers according to the embodiment of the invention. As shown in fig. 5, when the absolute values of the residual error values of the temperature difference between the inlet air and the outlet air and the heat exchange end difference of the surface air cooler both exceed the threshold, the third failure probability F of the air supply sensor among the plurality of sensors is determined 3 Increasing the preset probability. The preset probability may be set to 30%, for example.
If the absolute value of the residual error of the air inlet and outlet temperature difference exceeds a first threshold value and the absolute value of the residual error of the heat exchange end difference of the surface air cooler does not exceed the threshold value, determining a third fault probability F of the air mixing sensor in the plurality of sensors 3 Increasing the preset probability. The preset probability may be set to 30%, for example.
If the absolute value of the difference between the heat exchange ends of the surface air coolers exceeds a first threshold value and the absolute value of the residual error of the temperature difference between the inlet air and the outlet air does not exceed the threshold value, determining a third failure probability F of the frozen water supply water temperature sensor in the sensors 3 Increasing the preset probability. The preset probability may be set to 30%, for example.
While the third failure probability of the other sensors of the plurality of sensors that do not meet the above condition may be set to none or 0. It should be noted that the percentage of the operation parameter data satisfying the condition in fig. 5 needs to exceed a preset percentage to meet the calculation condition of the third failure probability.
In step 310, the fault diagnosis device 110 may determine a fourth fault probability of a part of the sensors in the plurality of sensors based on the constraint condition of the operation of the system under test, so as to locate an abnormal sensor of the system under test based on the first fault probability, the second fault probability, the third fault probability and the fourth fault probability.
In one embodiment, the fault diagnosis device 110 may be based on a constraint condition of the operation of the system to be tested, that is, a relationship between a mixed air temperature of the system to be tested and a fresh air temperature and a return air temperature of the system to be tested; the relationship between the air mixing humidity of the tested system and the fresh air humidity and the return air humidity of the tested system; the relationship between the air supply temperature of the system to be tested and the air mixing temperature of the system to be tested and the chilled water supply temperature of the system to be tested; and determining the fourth failure probability of a part of the sensors in the plurality of sensors according to the relation between the air supply humidity of the tested system and the air mixing humidity of the tested system.
If the mixed air temperature is not between the fresh air temperature and the return air temperature, the fresh air sensor, the return air sensor and the mixed air sensor of the tested system determine that the fourth failure probability F exists 4 . Fourth failure probability F 4 The preset probability is increased by, for example, 30%.
If the mixed air humidity is not between the fresh air humidity and the return air humidity, the fresh air sensor, the return air sensor and the mixed air sensor determine that the fourth failure probability F exists 4 . Fourth failure probability F 4 The preset probability is increased by, for example, 30%.
If the air supply temperature is less than or equal to the water supply temperature or greater than the air mixing temperature, the air supply sensor, the air mixing sensor and the chilled water supply temperature sensor determine that a fourth failure probability F exists 4 . Fourth failure probability F 4 The preset probability is increased by, for example, 30%.
If the supply air humidity is greater than the mix air humidity, the supply air sensor and the mix air sensor determine that there is a fourth probability of failure F 4 . Fourth failure probability F 4 The preset probability is increased by, for example, 30%.
Note that the above predetermined probabilities can be dynamically adjusted according to the characteristics of the system under test.
Finally, the fault diagnosis device 110 may determine a first weight, a second weight, a third weight, and a fourth weight corresponding to the first fault probability, the second fault probability, the third fault probability, and the fourth fault probability according to the measured system characteristics; based on the first weight, the second weight, the third weight and the fourth weight, performing weighted addition on the first fault probability, the second fault probability, the third fault probability and the fourth fault probability to determine a total fault probability; locating a sensor of the plurality of sensors having a total failure probability greater than a first failure threshold as an anomalous sensor; locating a sensor of the plurality of sensors having a total failure probability less than a second failure threshold as a normal sensor; and locating sensors of the plurality of sensors having a total probability of failure less than a first failure threshold and greater than a second failure threshold as the interested sensors likely to have abnormal signs.
For example, if the total failure probability of a sensor of the plurality of sensors is greater than a first threshold (e.g., 80%), then the sensor is anomalous; if the probability of failure is less than a second threshold (e.g., 30%), then the sensor is not anomalous; if the failure probability is larger than or equal to the second threshold and smaller than or equal to the first threshold, the sensor has abnormal symptoms, and follow-up attention needs to be paid.
By adopting the means, the method and the device can complete the online diagnosis of the sensor included in the tested equipment by utilizing the real-time operation parameters of the tested equipment, so that the sensor with a fault can be found in time. According to the invention, additional sensors such as water flow or wind speed are not required to be added, the on-line detection of a fault sensor can be completed by reading the operation parameters of the tail end combined type air conditioning box and combining a professional mechanism model based on principal component analysis, the abnormal sensor can be found in time, the accuracy and reliability of data are ensured, and effective data support is provided for the safe operation and energy-saving control strategy of the unit.
FIG. 6 illustrates a schematic block diagram of an example electronic device 600 that can be used to implement embodiments of the present disclosure. For example, the fault diagnosis device 110 as shown in fig. 1 may be implemented by the electronic device 600. As shown, electronic device 600 includes a Central Processing Unit (CPU) 601 that can perform various appropriate actions and processes according to computer program instructions stored in a Read Only Memory (ROM) 602 or loaded from a storage unit 608 into a Random Access Memory (RAM) 603. In the random access memory 603, various programs and data required for the operation of the electronic apparatus 600 can also be stored. The central processing unit 601, the read only memory 602, and the random access memory 603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
A number of components in the electronic device 600 are connected to the input/output interface 605, including: an input unit 606 such as a keyboard, a mouse, a microphone, and the like; an output unit 607 such as various types of displays, speakers, and the like; a storage unit 608, such as a magnetic disk, optical disk, or the like; and a communication unit 609 such as a network card, modem, wireless communication transceiver, etc. The communication unit 609 allows the device 600 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.
The various processes and processes described above, such as method 300 and method 400, may be performed by the central processing unit 601. For example, in some embodiments, methods 300 and 400 may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as storage unit 608. In some embodiments, part or all of the computer program may be loaded and/or installed onto the device 600 via the read only memory 602 and/or the communication unit 609. When the computer program is loaded into the random access memory 603 and executed by the central processing unit 601, one or more of the actions of the methods 300 and 400 described above may be performed.
The present disclosure relates to methods, apparatuses, systems, electronic devices, computer-readable storage media and/or computer program products. The computer program product may include computer-readable program instructions for performing various aspects of the present disclosure.
The computer-readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as a punch card or an in-groove protruding structure with instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or electrical signals transmitted through electrical wires.
The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or to an external computer or external storage device via a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge computing devices. The network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing/processing device.
Computer program instructions for carrying out operations of the present disclosure may be assembler instructions, instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, the electronic circuitry that can execute the computer-readable program instructions implements aspects of the present disclosure by utilizing the state information of the computer-readable program instructions to personalize the electronic circuitry, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA).
Various aspects of the present disclosure are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions.
These computer-readable program instructions may be provided to a processing unit of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processing unit of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Having described embodiments of the present disclosure, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen in order to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.
Claims (11)
1. A method for locating an anomalous sensor in a system under test, comprising:
acquiring a first detection model, a second detection model and a corresponding statistic threshold;
acquiring a plurality of groups of data to be detected from a plurality of sensors of a system to be detected, and analyzing the data to be detected by using a first detection model and a second detection model so as to determine statistical values of the data to be detected corresponding to the first detection model and the second detection model and residual values corresponding to the second model;
determining a first failure probability and a second failure probability for each sensor of the plurality of sensors corresponding to the first detection model and the second detection model based on the statistic threshold value and the statistic value;
determining a third probability of failure for a portion of the plurality of sensors based on the second probability of failure and the residual values corresponding to the second detection model; and
and determining a fourth fault probability of a part of sensors in the plurality of sensors based on the constraint condition of the operation of the system to be tested, so as to locate the abnormal sensor of the system to be tested based on the first fault probability, the second fault probability, the third fault probability and the fourth fault probability.
2. The method of claim 1, wherein the constraints on the operation of the system under test comprise:
the air mixing temperature of the tested system is between the fresh air temperature and the return air temperature of the tested system;
the air mixing humidity of the tested system is between the fresh air humidity and the return air humidity of the tested system;
the air supply temperature of the system to be tested is lower than the air mixing temperature of the system to be tested and higher than the chilled water supply temperature of the system to be tested; and
the air supply humidity of the tested system is lower than the air mixing humidity of the tested system.
3. The method of claim 1, wherein the system under test is a central air conditioning system, the plurality of sensors comprising:
fresh air sensor, return air sensor, mix wind sensor, air supply sensor, refrigerated water supply temperature sensor.
4. The method of claim 1, wherein locating an anomalous sensor of the system under test based on the first probability of failure, the second probability of failure, the third probability of failure, and the fourth probability of failure comprises:
determining a first weight, a second weight, a third weight and a fourth weight corresponding to the first failure probability, the second failure probability, the third failure probability and the fourth failure probability according to the characteristics of the system to be tested;
based on the first weight, the second weight, the third weight and the fourth weight, performing weighted addition on the first fault probability, the second fault probability, the third fault probability and the fourth fault probability to determine a total fault probability;
locating a sensor of the plurality of sensors having a total failure probability greater than a first failure threshold as an anomalous sensor;
locating a sensor of the plurality of sensors having a total failure probability less than a second failure threshold as a normal sensor; and
and positioning the sensors with the total failure probability less than or equal to the first failure threshold value and greater than or equal to the second failure threshold value in the plurality of sensors as the concerned sensors with possible abnormal signs.
5. The method of claim 1, wherein the first detection model and/or the second detection model is constructed by:
acquiring a plurality of sets of first operating parameters from a plurality of sensors of a system under test;
based on the constraint conditions of the operation of the system to be tested, screening multiple groups of acquired first operation parameters to acquire screened first operation parameters;
determining a second operating parameter based on the screened first operating parameter;
establishing a first data matrix from the screened first operating parameters and establishing a second data matrix from the screened first and second operating parameters; and
and determining principal component vectors, residual vectors and statistic thresholds corresponding to the first data matrix and the second data matrix so as to obtain a first detection model and a second detection model.
6. The method of claim 5, wherein the first operating parameter comprises:
fresh air temperature, fresh air humidity, return air temperature, return air humidity, mixed air temperature, mixed air humidity, fresh air valve opening, fan power, air supply temperature, air supply humidity, chilled water supply temperature, water pump power, water valve opening and bypass valve opening.
7. The method of claim 6, wherein the second operating parameter comprises:
determining an inlet and outlet air temperature difference based on the mixed air temperature and the air supply temperature; and
and the surface cooler heat exchange end difference is determined based on the air supply temperature and the chilled water supply temperature.
8. The method of claim 7, wherein obtaining the first detection model and the second detection model comprises:
establishing a first detection model based on one or more of the following operating parameters: fresh air temperature, fresh air humidity, return air temperature, return air humidity, mixed air temperature, mixed air humidity, fresh air valve opening degree and fan power.
9. The method of claim 8, wherein obtaining the first detection model and the second detection model comprises:
establishing a second detection model based on one or more of the following operating parameters: the air mixing temperature, the air mixing humidity, the air supply temperature, the air supply humidity, the chilled water supply temperature, the fan power, the water pump power, the air inlet and outlet temperature difference, the surface air cooler heat exchange end difference, the water valve opening degree and the bypass valve opening degree.
10. A computing device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor;
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-9.
11. A non-transitory computer readable storage medium having stored thereon computer instructions for causing a computer to perform the method of any one of claims 1-9.
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