CN116754016A - Fault detection method and device, electronic equipment and storage medium - Google Patents
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Abstract
The disclosure provides a fault detection method, a fault detection device, electronic equipment and a storage medium, and relates to the technical field of sensors. The method comprises the following steps: acquiring a plurality of sensors deployed on equipment to be detected and data to be processed, which are respectively acquired at the same time; inputting a plurality of data to be processed into an encoder in a self-encoder, and enabling the encoder to jointly encode the plurality of data to be processed to acquire a feature vector output by the encoder, wherein the self-encoder is generated based on training of a first sample data set acquired by a plurality of sensors in a first preset time period of normal operation; and according to the feature vector, performing fault detection on the equipment to be detected. Therefore, the plurality of sensors are combined and coded to perform fault detection on equipment to be detected, so that performance degradation of a certain sensor is avoided, errors caused by fault detection are avoided, frequent calibration of the sensors is not needed, and the equipment can be accurately subjected to fault detection.
Description
Technical Field
The disclosure relates to the technical field of sensors, and in particular relates to a fault detection method, a fault detection device, electronic equipment and a storage medium.
Background
The sensor is a hardware device, and is usually installed in a fixed place to collect specific indexes (for example, the sensor is installed on industrial equipment to monitor the running state of the industrial equipment), and the accuracy of the sensor is seriously affected due to the changing factors such as working environment, equipment aging and the like. Especially when the working environment changes severely (such as a desert fan vibration sensor, a large day-night temperature difference, a large seasonal change, a large wind power and other climatic conditions and a large fan running condition change), the problem is more remarkable.
Conventional sensor devices typically require calibration of the operating zero and operating curve at intervals or direct replacement of the sensor to meet the requirements of high accuracy long-term detection. However, periodic manual calibration incurs significant operating and maintenance costs, and is not suitable for manual field calibration in some complex working environments.
Disclosure of Invention
The present disclosure aims to solve, at least to some extent, one of the technical problems in the related art.
An embodiment of a first aspect of the present disclosure provides a fault detection method, including:
acquiring a plurality of sensors deployed on equipment to be detected and data to be processed, which are respectively acquired at the same time;
inputting a plurality of data to be processed into an encoder in a self-encoder, and enabling the encoder to jointly encode the plurality of data to be processed to acquire a feature vector output by the encoder, wherein the self-encoder is generated based on training of a first sample data set acquired by the plurality of sensors in a first preset time period of normal operation;
and carrying out fault detection on the equipment to be detected according to the feature vector.
An embodiment of a second aspect of the present disclosure proposes a fault detection device, including:
the first acquisition module is used for acquiring a plurality of sensors deployed on equipment to be detected and data to be processed, which are respectively acquired at the same time;
the second acquisition module is used for inputting a plurality of data to be processed into an encoder in the encoder, so that the encoder carries out joint encoding on the plurality of data to be processed to acquire a feature vector output by the encoder, wherein the self-encoder is generated based on training of a first sample data set acquired by the plurality of sensors in a first preset time period of normal operation;
and the detection module is used for carrying out fault detection on the equipment to be detected according to the characteristic vector.
An embodiment of a third aspect of the present disclosure provides an electronic device, including: the fault detection device comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor realizes the fault detection method as set forth in the embodiment of the first aspect of the disclosure when the processor executes the program.
An embodiment of a fourth aspect of the present disclosure proposes a computer readable storage medium storing a computer program which, when executed by a processor, implements a fault detection method as proposed by an embodiment of the first aspect of the present disclosure.
The fault detection method, the fault detection device, the electronic equipment and the storage medium have the following beneficial effects:
in the embodiment of the disclosure, a plurality of sensors deployed on equipment to be detected and data to be processed respectively acquired at the same time are firstly acquired, then the plurality of data to be processed are input into an encoder in a self-encoder, the encoder is enabled to perform joint encoding on the plurality of data to be processed so as to acquire a feature vector output by the encoder, and finally fault detection is performed on the equipment to be detected according to the feature vector. Therefore, the plurality of sensors are combined and coded to perform fault detection on equipment to be detected, so that performance degradation of a certain sensor is avoided, errors caused by fault detection are avoided, frequent calibration of the sensors is not needed, and the equipment can be accurately subjected to fault detection.
Additional aspects and advantages of the disclosure will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the disclosure.
Drawings
The foregoing and/or additional aspects and advantages of the present disclosure will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings, in which:
FIG. 1 is a flow chart of a fault detection method according to an embodiment of the disclosure;
FIG. 2 is a flow chart of a fault detection method according to another embodiment of the present disclosure;
FIG. 3 is a schematic diagram of a self-encoder according to an embodiment of the present disclosure;
FIG. 4 is a schematic structural diagram of a fault detection device according to an embodiment of the disclosure;
fig. 5 illustrates a block diagram of an exemplary electronic device suitable for use in implementing embodiments of the present disclosure.
Detailed Description
Embodiments of the present disclosure are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are exemplary and intended for the purpose of explaining the present disclosure and are not to be construed as limiting the present disclosure.
The following describes a fault detection method, apparatus, electronic device, and storage medium of the embodiments of the present disclosure with reference to the accompanying drawings.
Fig. 1 is a flow chart of a fault detection method according to an embodiment of the disclosure.
The embodiments of the present disclosure are exemplified in that the fault detection method is configured in a fault detection apparatus that can be applied to any electronic device so that the electronic device can perform a fault detection function.
The electronic device may be a personal computer (Personal Computer, abbreviated as PC), a cloud device, a mobile device, etc., and the mobile device may be a mobile phone, a tablet computer, a personal digital assistant, a wearable device, a vehicle-mounted device, etc. with various hardware devices including an operating system, a touch screen, and/or a display screen.
As shown in fig. 1, the fault detection method may include the steps of:
step 101, acquiring a plurality of sensors deployed on equipment to be detected and data to be processed, which are respectively acquired at the same time.
The device to be detected can be various industrial devices, such as a conveyor, a conveyor belt, etc.
Alternatively, the plurality of sensors comprises different types of sensors, such as acoustic sensors, vibration sensors, temperature sensors, and the like. In the embodiment of the disclosure, the number of each type of sensor is not limited, and may be one or more.
For example, the data to be processed collected by the sound sensor may be audio; the data collected by the temperature sensor may be temperature.
Step 102, inputting a plurality of data to be processed from an encoder in the encoder, so that the encoder performs joint encoding on the plurality of data to be processed to obtain a feature vector output by the encoder.
The self-encoder is generated based on training of the collected first sample data set by the plurality of sensors in a first preset time period of normal operation.
In the disclosed example, before the data to be processed is input to the self-encoder, the initial self-encoder is trained based on the first sample data set to generate a self-encoder with better performance for fault detection of the equipment to be detected.
It should be noted that the minimum failure-free time of the sensor may be as long as several months. Therefore, the data included in the first sample data set may be data acquired by the plurality of sensors in the first preset period of time after the plurality of sensors are deployed on the device to be detected and when no offset (i.e., normal operation) occurs.
For example, data acquired within one month after a plurality of sensors are deployed on the device to be detected, or within one week, is used as the first sample data set.
The more data of the sensor, the more complex the training of the self-encoder, and the more sample data is required to obtain the self-encoder with better performance. Accordingly, the first preset time period may be determined based on the number of the plurality of sensors. The greater the number of sensors, the longer the first preset time period; the smaller the number of sensors, the shorter the first preset time period.
And 103, carrying out fault detection on the equipment to be detected according to the feature vector.
Optionally, the feature vector is mapped to the feature space in a linear manner, and if the position of the feature vector in the feature space exceeds a preset space range, the fault of the equipment to be detected is determined.
The preset spatial range may be a spatial range in which all corresponding feature vectors are located when the device to be detected fails before the current moment.
Generally, there is more than one sensor deployed on the device to be detected, and the types, processes and degradation times of the sensors are different, so that the data of the plurality of sensors can play roles of mutual backup and cross-validation in monitoring the state of the device to be detected. In the embodiment of the disclosure, based on a self-encoder, to-be-processed data acquired by a plurality of sensors are subjected to joint encoding, feature vectors generated by the joint encoding are analyzed, and the state of equipment is monitored. Therefore, even if a certain sensor is offset, the feature vectors of the multi-sensor after mutual calibration are obtained through the joint analysis of the multi-dimensional sensor, and then the fault detection is carried out on the equipment, so that the numerical deviation caused by the performance degradation of the certain sensor can be avoided, and the method has stronger robustness.
In the embodiment of the disclosure, a plurality of sensors deployed on equipment to be detected and data to be processed respectively acquired at the same time are firstly acquired, then the plurality of data to be processed are input into an encoder in a self-encoder, the encoder is enabled to perform joint encoding on the plurality of data to be processed so as to acquire a feature vector output by the encoder, and finally fault detection is performed on the equipment to be detected according to the feature vector. Therefore, the plurality of sensors are combined and coded to perform fault detection on equipment to be detected, so that performance degradation of a certain sensor is avoided, errors caused by fault detection are avoided, frequent calibration of the sensors is not needed, and the equipment can be accurately subjected to fault detection.
Fig. 2 is a flow chart of a fault detection method according to an embodiment of the disclosure, as shown in fig. 2, the fault detection method may include the following steps:
step 201, acquiring a plurality of sensors deployed on equipment to be detected and data to be processed, which are respectively acquired at the same time.
Step 202, inputting a plurality of data to be processed from an encoder in the encoder, so that the encoder performs joint encoding on the plurality of data to be processed to obtain a feature vector output by the encoder.
The specific implementation manners of steps 201 to 202 may also refer to the detailed descriptions in other embodiments in the disclosure, and are not described in detail herein.
In step 203, the feature vector is input from the decoder in the encoder to obtain the prediction data corresponding to each sensor output by the decoder.
In the embodiment of the disclosure, the characteristic of the self-encoder can be utilized, and the decoder in the self-encoder is utilized to decode the feature vector so as to obtain reconstructed data, namely prediction data, corresponding to the data to be processed, acquired by each sensor.
Step 204, acquiring a second sample data set acquired by the plurality of sensors in a second preset time period before the current moment when the difference between any predicted data and the corresponding data to be processed is greater than a first threshold value.
Fig. 3 is a schematic diagram of a self-encoder according to an embodiment of the disclosure, as shown in fig. 3, taking an example in which 15 sensors are disposed on a device to be detected, x1 to x15 are data to be processed, h1 to h8 are feature vectors output from an encoder in the encoder, and x1 'to x15' are prediction data corresponding to each data to be processed. If the difference between x5 and x5' is greater than the first threshold, it indicates that a numerical deviation occurs in the sensor that acquired x 5. The self-encoder can be further modified to enable the self-encoder to have sensor characteristics after offset, so that the performance of the self-encoder is improved.
The self-encoder is modified and the sample dataset needs to be retrieved. In the embodiment of the disclosure, the data collected by each sensor may be obtained as a second sample data set in a second preset time period before the current time.
The current time may be a time when it is determined that a difference between any predicted data and corresponding data to be processed is greater than a first threshold.
Since the prior self-encoder has better performance, only a small part of sample data is needed to correct the self-encoder. Thus, the first preset time period may be greater than the second preset time period.
For example, the first preset time period may be one month, and the second preset time period may be one week, or half month.
Step 205, correcting the self-encoder based on the second sample data set.
That is, the self-encoder is trained again based on the second sample data set so that the self-encoder can learn the characteristics of the sensor after the numerical shift.
Furthermore, when the self-encoder is corrected, the fault detection can be carried out on the equipment to be detected based on the corrected self-encoder, so that the accuracy of the fault detection on the follow-up equipment to be detected is ensured.
Since the degradation of the sensor is slow, there is little change over a relatively small (e.g., several months) period; in addition, the sensor's operating curve is typically subject to global deflection or local deformation, but does not affect the relative magnitude of the different real data reflected on the sensor, and the physical characteristics of the monitored values can still be reflected by the relative magnitude of the sensor values in a short period of time.
In the embodiment of the disclosure, the self-encoder can be finely tuned by using a small amount of data in a short period, the required data amount is small, the fine tuning is quick, and the characteristic vector can also be enabled to rapidly reflect the sensor characteristic after the offset occurs, so that the problem of numerical value offset of the sensor due to performance degradation can be solved.
In the embodiment of the disclosure, a plurality of sensors deployed on equipment to be detected and data to be processed respectively acquired at the same time are acquired; and inputting the plurality of data to be processed into an encoder in the encoder, enabling the encoder to jointly encode the plurality of data to be processed to obtain a feature vector output by the encoder, inputting the feature vector into a decoder in the encoder to obtain prediction data corresponding to each sensor output by the decoder, obtaining a second sample data set acquired by the plurality of sensors in a second preset time period before the current moment under the condition that the difference between any prediction data and the corresponding data to be processed is larger than a first threshold value, and finally correcting the self-encoder based on the second sample data set. Therefore, under the condition that the performance of a certain sensor is degraded and data deviation occurs, the self-encoder can be corrected based on the second sample data set, so that the self-encoder can learn the characteristic of the sensor after the value deviation occurs, and further the accuracy of fault detection of the equipment to be detected is guaranteed.
In order to implement the above embodiment, the present disclosure also proposes a fault detection device.
Fig. 4 is a schematic structural diagram of a fault detection device according to an embodiment of the present disclosure.
As shown in fig. 4, the fault detection apparatus 400 may include: .
A first obtaining module 410, configured to obtain a plurality of sensors deployed on a device to be detected, and data to be processed acquired respectively at the same time;
the second obtaining module 420 is configured to input a plurality of data to be processed into an encoder in the self-encoder, so that the encoder jointly encodes the plurality of data to be processed to obtain a feature vector output by the encoder, where the self-encoder is generated based on training of a first sample data set acquired by a plurality of sensors in a first preset time period of normal operation;
and the detection module 430 is configured to perform fault detection on the device to be detected according to the feature vector.
Optionally, the device further comprises a correction module for:
inputting the feature vector from a decoder in the encoder to obtain prediction data corresponding to each sensor output by the decoder;
acquiring a second sample data set acquired by a plurality of sensors in a second preset time period before the current moment under the condition that the difference between any predicted data and corresponding data to be processed is larger than a first threshold value;
the self-encoder is modified based on the second sample data set.
Optionally, the first preset time period is greater than the second preset time period.
Optionally, the detection module 440 is configured to:
linearly mapping the feature vector to a feature space;
if the position of the feature vector in the feature space exceeds the preset space range, determining that the equipment to be detected has faults.
Optionally, the plurality of sensors comprises different types of sensors.
Optionally, the method further comprises:
a first preset time period is determined based on the number of the plurality of sensors.
The functions and specific implementation principles of the foregoing modules in the embodiments of the present disclosure may refer to the foregoing method embodiments, and are not repeated herein.
According to the fault detection device, firstly, a plurality of sensors deployed on equipment to be detected and data to be processed are acquired respectively at the same time, then the data to be processed are input into an encoder in the encoder, the encoder is enabled to perform joint encoding on the data to be processed, so that feature vectors output by the encoder are acquired, and finally, fault detection is performed on the equipment to be detected according to the feature vectors. Therefore, the plurality of sensors are combined and coded to perform fault detection on equipment to be detected, so that performance degradation of a certain sensor is avoided, errors caused by fault detection are avoided, frequent calibration of the sensors is not needed, and the equipment can be accurately subjected to fault detection.
In order to achieve the above embodiments, the present disclosure further proposes an electronic device including: the fault detection method according to the foregoing embodiments of the present disclosure is implemented by a memory, a processor, and a computer program stored in the memory and executable on the processor, when the processor executes the program.
In order to implement the above-mentioned embodiments, the present disclosure also proposes a computer-readable storage medium storing a computer program which, when executed by a processor, implements a fault detection method as proposed in the foregoing embodiments of the present disclosure.
Fig. 5 illustrates a block diagram of an exemplary electronic device suitable for use in implementing embodiments of the present disclosure. The electronic device 12 shown in fig. 5 is merely an example and should not be construed to limit the functionality and scope of use of embodiments of the present disclosure in any way.
As shown in fig. 5, the electronic device 12 is in the form of a general purpose computing device. Components of the electronic device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, a bus 18 that connects the various system components, including the system memory 28 and the processing units 16.
Bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, and a local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include industry Standard architecture (Industry Standard Architecture; hereinafter ISA) bus, micro channel architecture (Micro Channel Architecture; hereinafter MAC) bus, enhanced ISA bus, video electronics standards Association (Video Electronics Standards Association; hereinafter VESA) local bus, and peripheral component interconnect (Peripheral Component Interconnection; hereinafter PCI) bus.
Electronic device 12 typically includes a variety of computer system readable media. Such media can be any available media that is accessible by electronic device 12 and includes both volatile and nonvolatile media, removable and non-removable media.
Memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (Random Access Memory; hereinafter: RAM) 30 and/or cache memory 32. The electronic device 12 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 34 may be used to read from or write to non-removable, nonvolatile magnetic media (not shown in FIG. 5, commonly referred to as a "hard disk drive"). Although not shown in fig. 5, a magnetic disk drive for reading from and writing to a removable non-volatile magnetic disk (e.g., a "floppy disk"), and an optical disk drive for reading from or writing to a removable non-volatile optical disk (e.g., a compact disk read only memory (Compact Disc Read Only Memory; hereinafter CD-ROM), digital versatile read only optical disk (Digital Video Disc Read Only Memory; hereinafter DVD-ROM), or other optical media) may be provided. In such cases, each drive may be coupled to bus 18 through one or more data medium interfaces. Memory 28 may include at least one program product having a set (e.g., at least one) of program modules configured to carry out the functions of the various embodiments of the disclosure.
A program/utility 40 having a set (at least one) of program modules 42 may be stored in, for example, memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment. Program modules 42 generally perform the functions and/or methods in the embodiments described in this disclosure.
The electronic device 12 may also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), one or more devices that enable a user to interact with the electronic device 12, and/or any devices (e.g., network card, modem, etc.) that enable the electronic device 12 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 22. Also, the electronic device 12 may communicate with one or more networks, such as a local area network (Local Area Network; hereinafter: LAN), a wide area network (Wide Area Network; hereinafter: WAN) and/or a public network, such as the Internet, via the network adapter 20. As shown, the network adapter 20 communicates with other modules of the electronic device 12 over the bus 18. It should be appreciated that although not shown, other hardware and/or software modules may be used in connection with electronic device 12, including, but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.
The processing unit 16 executes various functional applications and data processing by running programs stored in the system memory 28, for example, implementing the methods mentioned in the foregoing embodiments.
According to the technical scheme, a plurality of sensors deployed on equipment to be detected and data to be processed, which are respectively collected at the same time, are firstly obtained, then the data to be processed are input into an encoder in a self-encoder, the encoder is enabled to jointly encode the data to be processed, so that feature vectors output by the encoder are obtained, and finally fault detection is carried out on the equipment to be detected according to the feature vectors. Therefore, the plurality of sensors are combined and coded to perform fault detection on equipment to be detected, so that performance degradation of a certain sensor is avoided, errors caused by fault detection are avoided, frequent calibration of the sensors is not needed, and the equipment can be accurately subjected to fault detection.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present disclosure. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present disclosure, the meaning of "a plurality" is at least two, such as two, three, etc., unless explicitly specified otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and additional implementations are included within the scope of the preferred embodiment of the present disclosure in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the embodiments of the present disclosure.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer readable medium may even be paper or other suitable medium on which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
It should be understood that portions of the present disclosure may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. As with the other embodiments, if implemented in hardware, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
Those of ordinary skill in the art will appreciate that all or a portion of the steps carried out in the method of the above-described embodiments may be implemented by a program to instruct related hardware, where the program may be stored in a computer readable storage medium, and where the program, when executed, includes one or a combination of the steps of the method embodiments.
Furthermore, each functional unit in the embodiments of the present disclosure may be integrated in one processing module, or each unit may exist alone physically, or two or more units may be integrated in one module. The integrated modules may be implemented in hardware or in software functional modules. The integrated modules may also be stored in a computer readable storage medium if implemented in the form of software functional modules and sold or used as a stand-alone product.
The above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, or the like. Although embodiments of the present disclosure have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the present disclosure, and that variations, modifications, alternatives, and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the present disclosure.
Claims (10)
1. A method of fault detection, comprising:
acquiring a plurality of sensors deployed on equipment to be detected and data to be processed, which are respectively acquired at the same time;
inputting a plurality of data to be processed into an encoder in a self-encoder, and enabling the encoder to jointly encode the plurality of data to be processed to acquire a feature vector output by the encoder, wherein the self-encoder is generated based on training of a first sample data set acquired by the plurality of sensors in a first preset time period of normal operation;
and carrying out fault detection on the equipment to be detected according to the feature vector.
2. The method as recited in claim 1, further comprising:
inputting the feature vector into a decoder in the self-encoder to acquire prediction data corresponding to each sensor output by the decoder;
acquiring a second sample data set acquired by the plurality of sensors in a second preset time period before the current moment under the condition that the difference between any predicted data and corresponding data to be processed is larger than a first threshold value;
the self-encoder is modified based on the second sample data set.
3. The method of claim 2, wherein the first preset time period is greater than the second preset time period.
4. The method according to claim 1, wherein the fault detection of the device to be detected according to the feature vector comprises:
linearly mapping the feature vector to a feature space;
and if the position of the feature vector in the feature space exceeds a preset space range, determining that the equipment to be detected has faults.
5. The method of any one of claims 1-4, wherein the plurality of sensors comprises different types of sensors.
6. The method of any one of claims 1-4, further comprising:
the first preset time period is determined based on the number of the plurality of sensors.
7. A device for fault detection, comprising:
the first acquisition module is used for acquiring a plurality of sensors deployed on equipment to be detected and data to be processed, which are respectively acquired at the same time;
the second acquisition module is used for inputting a plurality of data to be processed into an encoder in the encoder, so that the encoder carries out joint encoding on the plurality of data to be processed to acquire a feature vector output by the encoder, wherein the self-encoder is generated based on training of a first sample data set acquired by the plurality of sensors in a first preset time period of normal operation;
and the detection module is used for carrying out fault detection on the equipment to be detected according to the characteristic vector.
8. The apparatus of claim 7, further comprising a correction module for:
inputting the feature vector into a decoder in the self-encoder to acquire prediction data corresponding to each sensor output by the decoder;
acquiring a second sample data set acquired by the plurality of sensors in a second preset time period before the current moment under the condition that the difference between any predicted data and corresponding data to be processed is larger than a first threshold value;
the self-encoder is modified based on the second sample data set.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the fault detection method according to any one of claims 1-6 when the program is executed by the processor.
10. A computer readable storage medium storing a computer program, wherein the computer program when executed by a processor implements the fault detection method according to any one of claims 1-6.
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| CN202310756200.9A CN116754016B (en) | 2023-06-25 | Fault detection method and device, electronic equipment and storage medium |
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Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107702922A (en) * | 2017-09-11 | 2018-02-16 | 南京信息工程大学 | Based on LCD and the Fault Diagnosis of Roller Bearings for stacking autocoder |
| CN110207997A (en) * | 2019-07-24 | 2019-09-06 | 中国人民解放军国防科技大学 | Liquid rocket engine fault detection method based on convolution self-encoder |
| CN111767183A (en) * | 2020-09-01 | 2020-10-13 | 深圳创新奇智科技有限公司 | Equipment abnormality detection method and device, electronic equipment and storage medium |
| CN112702329A (en) * | 2020-12-21 | 2021-04-23 | 四川虹微技术有限公司 | Traffic data anomaly detection method and device and storage medium |
| CN112904810A (en) * | 2021-01-13 | 2021-06-04 | 中南大学 | Process industry nonlinear process monitoring method based on effective feature selection |
| CN113193911A (en) * | 2021-04-13 | 2021-07-30 | 北京邮电大学 | Optical network fault detection method and system |
| CN113587362A (en) * | 2021-07-30 | 2021-11-02 | 广东美的暖通设备有限公司 | Abnormity detection method and device and air conditioning system |
| CN113962289A (en) * | 2021-09-26 | 2022-01-21 | 西安交通大学 | Lifelong learning-oriented online intelligent fault diagnosis method and system for rotary machine |
| CN114152825A (en) * | 2021-11-16 | 2022-03-08 | 国网北京市电力公司 | Fault diagnosis method and device of transformer and fault diagnosis system of transformer |
| CN116130029A (en) * | 2022-12-30 | 2023-05-16 | 广东核电合营有限公司 | Transformer fault early warning method, device, equipment, storage medium and program product |
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107702922A (en) * | 2017-09-11 | 2018-02-16 | 南京信息工程大学 | Based on LCD and the Fault Diagnosis of Roller Bearings for stacking autocoder |
| CN110207997A (en) * | 2019-07-24 | 2019-09-06 | 中国人民解放军国防科技大学 | Liquid rocket engine fault detection method based on convolution self-encoder |
| CN111767183A (en) * | 2020-09-01 | 2020-10-13 | 深圳创新奇智科技有限公司 | Equipment abnormality detection method and device, electronic equipment and storage medium |
| CN112702329A (en) * | 2020-12-21 | 2021-04-23 | 四川虹微技术有限公司 | Traffic data anomaly detection method and device and storage medium |
| CN112904810A (en) * | 2021-01-13 | 2021-06-04 | 中南大学 | Process industry nonlinear process monitoring method based on effective feature selection |
| CN113193911A (en) * | 2021-04-13 | 2021-07-30 | 北京邮电大学 | Optical network fault detection method and system |
| CN113587362A (en) * | 2021-07-30 | 2021-11-02 | 广东美的暖通设备有限公司 | Abnormity detection method and device and air conditioning system |
| CN113962289A (en) * | 2021-09-26 | 2022-01-21 | 西安交通大学 | Lifelong learning-oriented online intelligent fault diagnosis method and system for rotary machine |
| CN114152825A (en) * | 2021-11-16 | 2022-03-08 | 国网北京市电力公司 | Fault diagnosis method and device of transformer and fault diagnosis system of transformer |
| CN116130029A (en) * | 2022-12-30 | 2023-05-16 | 广东核电合营有限公司 | Transformer fault early warning method, device, equipment, storage medium and program product |
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