CN115993504B - Intelligent fault diagnosis method and system for electrical equipment - Google Patents
Intelligent fault diagnosis method and system for electrical equipment Download PDFInfo
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Abstract
The application discloses an intelligent fault diagnosis method and system of electrical equipment, and relates to the field of data processing, wherein the method comprises the following steps: signal acquisition is carried out on the electric operation loop, and a loop signal data set is obtained; signal acquisition is carried out on the real-time operation environment to obtain an environment signal data set; performing feature recognition on the environmental signal data set to generate environmental signal separation parameters; performing signal processing on the loop signal data set according to the environmental signal separation parameters to obtain a filtered signal data set; carrying out signal anomaly identification on the filtered signal data set to obtain a real-time anomaly signal set; and carrying out fault analysis on the real-time abnormal signal set to generate a device fault report. The technical problems that in the prior art, the fault diagnosis accuracy aiming at the electrical equipment is insufficient, and then the fault diagnosis effect of the electrical equipment is poor are solved. The method and the device have the advantages of improving the accuracy of fault diagnosis of the electrical equipment, improving the quality of fault diagnosis of the electrical equipment and the like.
Description
Technical Field
The application relates to the field of data processing, in particular to an intelligent fault diagnosis method and system for electrical equipment.
Background
The electrical equipment is widely applied to the production and the living of people and plays a great value and role. However, various failures are always unavoidable during the use of the electrical device. The traditional electrical equipment fault diagnosis method has the defects of strong environmental interference, low diagnosis precision and the like. The research design of the method for optimizing fault diagnosis of the electrical equipment has important practical significance.
In the prior art, the fault diagnosis accuracy of the electrical equipment is insufficient, and the technical problem of poor fault diagnosis effect of the electrical equipment is caused.
Disclosure of Invention
The application provides an intelligent fault diagnosis method and system for electrical equipment. The technical problems that in the prior art, the fault diagnosis accuracy aiming at the electrical equipment is insufficient, and then the fault diagnosis effect of the electrical equipment is poor are solved. The intelligent, reliable and accurate fault analysis is performed on the electrical equipment, the fault diagnosis accuracy of the electrical equipment is improved, the fault diagnosis quality of the electrical equipment is improved, and a powerful guarantee technical effect is provided for the normal operation of the electrical equipment.
In view of the above problems, the present application provides an intelligent fault diagnosis method and system for electrical equipment.
In a first aspect, the present application provides an intelligent fault diagnosis method for an electrical apparatus, where the method is applied to an intelligent fault diagnosis system for an electrical apparatus, the method includes: acquiring an electric operation loop according to each component of the first electric equipment; the electric operation loop is subjected to signal acquisition according to the signal acquisition device, so that a loop signal data set is obtained; acquiring a real-time operation environment of the first electrical equipment; acquiring an environment signal data set by carrying out signal acquisition on the real-time operation environment; performing feature recognition on the environmental signal data set to generate environmental signal separation parameters; performing signal processing on the loop signal data set according to the environmental signal separation parameter to obtain a filtered signal data set; carrying out signal anomaly identification on the filtered signal data set to obtain a real-time anomaly signal set; and generating a device fault report by carrying out fault analysis on the real-time abnormal signal set.
In a second aspect, the present application also provides an intelligent fault diagnosis system for an electrical apparatus, wherein the system includes: the electric operation loop acquisition module is used for acquiring an electric operation loop according to each component of the first electric equipment; the loop signal acquisition module is used for acquiring signals of the electric operation loop according to the signal acquisition device to obtain a loop signal data set; the operation environment acquisition module is used for acquiring the real-time operation environment of the first electrical equipment; the environment signal acquisition module is used for acquiring an environment signal data set by carrying out signal acquisition on the real-time operation environment; the characteristic recognition module is used for carrying out characteristic recognition on the environmental signal data set and generating environmental signal separation parameters; the signal processing module is used for performing signal processing on the loop signal data set according to the environmental signal separation parameter to obtain a filtered signal data set; the signal abnormality identification module is used for carrying out signal abnormality identification on the filtered signal data set to obtain a real-time abnormal signal set; the fault analysis module is used for generating a device fault report by carrying out fault analysis on the real-time abnormal signal set.
In a third aspect, the present application also provides an electronic device, including: a memory for storing executable instructions; and the processor is used for realizing the intelligent fault diagnosis method of the electrical equipment when executing the executable instructions stored in the memory.
In a fourth aspect, the present application also provides a computer readable storage medium storing a computer program, which when executed by a processor, implements an intelligent fault diagnosis method for an electrical device provided by the present application.
One or more technical schemes provided by the application have at least the following technical effects or advantages:
the method comprises the steps that signal acquisition is conducted on an electric operation loop of first electric equipment through a signal acquisition device, and a loop signal data set is obtained; acquiring an environment signal data set by carrying out signal acquisition on the real-time operation environment of the first electric equipment; generating an environmental signal separation parameter by performing feature recognition on the environmental signal data set; performing signal processing on the loop signal data set according to the environmental signal separation parameters to obtain a filtered signal data set; acquiring a real-time abnormal signal set by carrying out signal abnormality identification on the filtered signal data set; and generating a device fault report by carrying out fault analysis on the real-time abnormal signal set. The intelligent, reliable and accurate fault analysis is performed on the electrical equipment, the fault diagnosis accuracy of the electrical equipment is improved, the fault diagnosis quality of the electrical equipment is improved, and a powerful guarantee technical effect is provided for the normal operation of the electrical equipment.
The foregoing description is only an overview of the present application, and is intended to be implemented in accordance with the teachings of the present application in order that the same may be more clearly understood and to make the same and other objects, features and advantages of the present application more readily apparent.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings of the embodiments of the present disclosure will be briefly described below. It is apparent that the figures in the following description relate only to some embodiments of the present disclosure and are not limiting of the present disclosure.
FIG. 1 is a schematic flow chart of an intelligent fault diagnosis method for an electrical device according to the present application;
FIG. 2 is a schematic flow chart of generating environmental signal separation parameters in an intelligent fault diagnosis method of an electrical device according to the present application;
FIG. 3 is a schematic diagram of an intelligent fault diagnosis system for an electrical device according to the present application;
fig. 4 is a schematic structural view of an exemplary electronic device of the present application.
Reference numerals illustrate: the system comprises an electric operation loop acquisition module 11, a loop signal acquisition module 12, an operation environment acquisition module 13, an environment signal acquisition module 14, a characteristic identification module 15, a signal processing module 16, a signal abnormality identification module 17, a fault analysis module 18, a processor 31, a memory 32, an input device 33 and an output device 34.
Detailed Description
The application provides an intelligent fault diagnosis method and system for electrical equipment. The technical problems that in the prior art, the fault diagnosis accuracy aiming at the electrical equipment is insufficient, and then the fault diagnosis effect of the electrical equipment is poor are solved. The intelligent, reliable and accurate fault analysis is performed on the electrical equipment, the fault diagnosis accuracy of the electrical equipment is improved, the fault diagnosis quality of the electrical equipment is improved, and a powerful guarantee technical effect is provided for the normal operation of the electrical equipment.
Example 1
Referring to fig. 1, the present application provides an intelligent fault diagnosis method for an electrical device, wherein the method is applied to an intelligent fault diagnosis system for an electrical device, the system is in communication connection with a signal acquisition device, and the method specifically includes the following steps:
step S100: acquiring an electric operation loop according to each component of the first electric equipment;
specifically, the first electric device is subjected to operation circuit analysis of each component member to obtain an electric operation circuit. Wherein the first electrical device includes any electrical device that performs intelligent fault analysis using the intelligent fault diagnosis system of the one electrical device. Each component member includes a plurality of component members such as a power supply member, an electricity consumption member, a power distribution member, and the like of the first electric apparatus. The electrical operating circuit includes a plurality of electrical operating circuits such as a plurality of high voltage electrical circuits, a plurality of magnetically induced electrical circuits, and the like of the first electrical device.
Further, the step S100 of the present application further includes:
step S110: acquiring a plurality of electric operation loops according to the connection relation of the component members;
step S120: performing signal interference analysis on the plurality of electric operation loops to obtain a plurality of signal interference indexes;
further, step S120 of the present application further includes:
step S121: acquiring voltage configuration data and magnetic induction configuration data of the plurality of electric operation loops;
step S122: inputting the voltage configuration data and the magnetic induction configuration data into a signal interference analysis model, and acquiring a high-voltage interference index and a magnetic induction interference index according to the signal interference analysis model;
step S123: and carrying out weight calculation by using the high-voltage interference index and the magnetic induction interference index to obtain the signal interference indexes.
Specifically, voltage configuration parameters and magnetic induction configuration parameters of a plurality of electric operation loops are collected, and voltage configuration data and magnetic induction configuration data are obtained. And further, taking the voltage configuration data and the magnetic induction configuration data as input information, inputting the input information into a signal interference analysis model to obtain a high-voltage interference index and a magnetic induction interference index, and carrying out weight calculation on the high-voltage interference index and the magnetic induction interference index to obtain a plurality of signal interference indexes.
The voltage configuration data comprise a plurality of historical real-time voltage parameters corresponding to a plurality of high-voltage electric loops in the first electric equipment under different working conditions. The magnetic induction configuration data comprises a plurality of historical real-time magnetic induction parameters corresponding to a plurality of magnetic induction electric loops in the first electric equipment under different working conditions. And carrying out historical data query based on the voltage configuration data and the magnetic induction configuration data to obtain a plurality of historical voltage configuration data, a plurality of historical magnetic induction configuration data, a plurality of historical high-voltage interference indexes and a plurality of historical magnetic induction interference indexes. And continuously self-training and learning the plurality of historical voltage configuration data, the plurality of historical magnetic induction configuration data, the plurality of historical high-voltage interference indexes and the plurality of historical magnetic induction interference indexes to a convergence state, so as to obtain the signal interference analysis model. The signal interference analysis model comprises an input layer, an implicit layer and an output layer. The signal interference analysis model has the functions of performing high-voltage analysis, magnetic susceptibility analysis and interference index evaluation on input voltage configuration data and magnetic susceptibility configuration data. The high voltage disturbance index is used for identifying the influence degree of the high voltage intensity on the signal output by the component. That is, the high voltage disturbance index includes a plurality of high voltage intensity influence parameters corresponding to a plurality of high voltage electric circuits. The greater the degree of influence of the high voltage intensity on the signal output by the member, the higher the corresponding high voltage intensity influence parameter. The magnetic induction interference index is used for identifying the influence degree of the magnetic field induction intensity on the signal output by the component. That is, the magnetic induction disturbance index includes a plurality of magnetic induction intensity influence parameters corresponding to a plurality of magnetic induction electric loops. The greater the degree of influence of the magnetic field induction intensity on the signal output by the component, the higher the corresponding magnetic field induction intensity influence parameter.
In an exemplary embodiment, when obtaining a plurality of signal-to-interference indexes, weight distribution is performed on a plurality of high-voltage strength influencing parameters and a plurality of magnetic field strength influencing parameters according to preset and determined high-voltage weight parameters and magnetic induction weight parameters, a plurality of high-voltage signal-to-interference indexes and a plurality of magnetic induction signal-to-interference indexes are obtained, and the plurality of high-voltage signal-to-interference indexes and the plurality of magnetic induction signal-to-interference indexes are output as a plurality of signal-to-interference indexes. The plurality of high voltage signal to interference indices includes a plurality of products between the high voltage weight parameter and the plurality of high voltage strength influencing parameters. The plurality of magnetic induction signal interference indices includes a plurality of products between a magnetic induction weight parameter and a plurality of magnetic field induction strength influencing parameters.
The method achieves the technical effects of accurately and efficiently analyzing the voltage configuration data and the magnetic induction analysis through the signal interference analysis model, obtaining reliable high-voltage interference index, magnetic induction interference index and a plurality of signal interference indexes, and improving the accuracy of fault diagnosis of electrical equipment.
Further, step S122 of the present application further includes:
step S1221: performing high-voltage performance analysis according to the voltage configuration data to obtain a high-voltage interference index, wherein the high-voltage interference index is used for identifying the influence degree of high-voltage intensity on signals output by the component;
step S1222: according to the high-voltage interference index, identifying N high-voltage electric circuits larger than a preset high-voltage interference index from the plurality of electric operation circuits;
step S1223: and generating signal sampling identifications based on the N high-voltage electric loops.
Specifically, whether the high-voltage interference index is larger than a preset high-voltage interference index is judged. And identifying the electric operation loops corresponding to the high-voltage interference indexes larger than the preset high-voltage interference index to obtain N high-voltage electric loops, and carrying out signal sampling identification on the N high-voltage electric loops. The preset high-voltage interference index comprises a preset and determined high-voltage interference index threshold value. The N high-voltage electric circuits comprise a plurality of electric operation circuits corresponding to the high-voltage interference indexes larger than the preset high-voltage interference index.
Further, step S122 of the present application further includes:
step S1224: performing magnetic susceptibility analysis according to the magnetic susceptibility configuration data to obtain a magnetic susceptibility disturbance index, wherein the magnetic susceptibility disturbance index is used for identifying the influence degree of magnetic field induction intensity on signals output by the component;
step S1225: according to the magnetic induction interference index, identifying N magnetic induction electric loops which are larger than a preset magnetic induction interference index from the electric operation loops;
step S1226: and generating a signal sampling identifier based on the N magnetic induction electric loops.
Specifically, it is determined whether or not the magnetic induction interference index is greater than a preset magnetic induction interference index. And identifying the electric operation loops corresponding to the magnetic induction interference indexes larger than the preset magnetic induction interference index to obtain N magnetic induction electric loops, and carrying out signal sampling identification on the N magnetic induction electric loops. The preset magnetic induction interference index comprises a preset fixed magnetic induction interference index threshold value. The N magnetic induction electric loops comprise a plurality of electric operation loops corresponding to magnetic induction interference indexes larger than a preset magnetic induction interference index.
Step S130: identifying the plurality of electric operation loops according to the plurality of signal interference indexes to obtain an identified electric operation loop;
step S140: and carrying out signal acquisition on the identification electric operation loop according to the signal acquisition device.
Step S200: the electric operation loop is subjected to signal acquisition according to the signal acquisition device, so that a loop signal data set is obtained;
specifically, a plurality of electrical operation loops are identified according to a plurality of signal interference indexes, and an identified electrical operation loop is obtained. And carrying out signal acquisition on the identification electric operation loop through a signal acquisition device to obtain a loop signal data set. The identification electric operation circuit comprises N high-voltage electric circuits and N magnetic induction electric circuits. The signal acquisition device comprises electromagnetic signal acquisition equipment in the prior art. The loop signal dataset includes a plurality of real-time voltage parameters and a plurality of real-time magnetic induction parameters corresponding to the identification electric operation loop. The technical effects of acquiring signals of the identification electric operation loop through the signal acquisition device to obtain a loop signal data set and laying a foundation for the follow-up fault analysis of the first electric equipment are achieved.
Step S300: acquiring a real-time operation environment of the first electrical equipment;
step S400: acquiring an environment signal data set by carrying out signal acquisition on the real-time operation environment;
specifically, signal acquisition is performed on a real-time operating environment of the first electrical device, and an environment signal data set is obtained. The real-time operation environment comprises a real-time operation position parameter, a real-time load parameter, a real-time environment temperature parameter, a real-time environment humidity parameter, a real-time working condition parameter and the like of the first electric equipment. The environment signal data set comprises real-time environment voltage parameters and real-time environment magnetic induction parameters corresponding to the real-time operation environment of the first electric equipment. The technical effects of acquiring an environment signal data set through signal acquisition of the real-time operation environment of the first electric equipment and providing data support for subsequent signal processing of the loop signal data set are achieved.
Step S500: performing feature recognition on the environmental signal data set to generate environmental signal separation parameters;
further, as shown in fig. 2, step S500 of the present application further includes:
step S510: noise identification is carried out on the environmental signal data set, and a plurality of noise sources are obtained;
step S520: acquiring noise characteristics by performing characteristic analysis on the plurality of noise sources;
step S530: analyzing the noise characteristics based on wavelet transformation, and determining denoising pretreatment parameters;
step S540: and taking the denoising pretreatment parameter as the environmental signal separation parameter, and performing environmental noise separation on the loop signal data set.
Step S600: performing signal processing on the loop signal data set according to the environmental signal separation parameter to obtain a filtered signal data set;
specifically, a plurality of noise sources are acquired by noise identification of an ambient signal dataset. Noise characteristics are obtained by performing a feature analysis on a plurality of noise sources. And carrying out denoising parameter matching on the noise characteristics through wavelet transformation to obtain denoising pretreatment parameters. And setting the denoising pretreatment parameter as an environmental signal separation parameter, and performing environmental noise separation on the loop signal data set through the environmental signal separation parameter to obtain a filtered signal data set.
The plurality of noise sources comprise a plurality of noise source information corresponding to a plurality of noise data in the environmental signal data set. The noise characteristics include noise frequency characteristics, noise loudness characteristics, noise propagation range characteristics, noise type characteristics, and the like, corresponding to the plurality of noise sources. The conventional denoising algorithm is easy to cause signal distortion while eliminating signal noise. The wavelet transformation is a time-frequency localization analysis method in which both the time domain window and the frequency domain window can be changed. The high-frequency signal denoising method has the advantages that the high-frequency signal denoising method has high frequency resolution and low time resolution in a low-frequency part, and the high-frequency signal denoising method has high time resolution and low frequency resolution in a high-frequency part, is very suitable for detecting transient abnormal phenomena carried in normal signals and displaying components of the transient abnormal phenomena, is favorable for separating noise from the normal signals, and realizes signal denoising. Wavelet transformation is a multi-resolution, multi-scale, constant-figure-of-merit denoising algorithm. The denoising pretreatment parameters comprise signal-to-noise ratio parameters, wavelet mother functions, wavelet coefficients, denoising strength and other wavelet treatment parameters corresponding to noise characteristics. The ambient signal separation parameters include denoising pre-processing parameters. The filtered signal data set includes a loop signal data set after ambient noise separation by an ambient signal separation parameter. The method achieves the technical effects of separating the environmental noise from the loop signal data set through the environmental signal separation parameters and obtaining the filtered signal data set, thereby reducing the interference of the environmental noise on the loop signal and improving the reliability and accuracy of fault diagnosis of the electrical equipment.
Step S700: carrying out signal anomaly identification on the filtered signal data set to obtain a real-time anomaly signal set;
step S800: and generating a device fault report by carrying out fault analysis on the real-time abnormal signal set.
Further, step S800 of the present application further includes:
step S810: collecting a historical anomaly signal set of the first electrical device;
step S820: classifying based on the historical abnormal signal sets to obtain multiple types of abnormal signal sets;
step S830: generating a mapping database of the abnormal signal and the abnormal equipment by using the multi-class abnormal signal set;
step S840: and carrying out fault analysis on the received real-time abnormal signal set according to the mapping database to generate the equipment fault report.
Specifically, the historical data of the abnormal signals of the first electrical equipment are collected to obtain a historical abnormal signal set, and the historical abnormal signal set is classified to obtain a plurality of types of abnormal signal sets. The sets of anomaly signals of multiple classes are added to a mapping database of anomaly signals to anomaly devices. Further, a real-time abnormal signal set is obtained by performing signal abnormality recognition on the filtered signal data set. And taking the real-time abnormal signal set as input information, inputting the input information into a mapping database of the abnormal signal and abnormal equipment, and obtaining an equipment fault report.
The historical abnormal signal set comprises a plurality of historical abnormal signals of the first electrical equipment, a plurality of historical abnormal signal types, a plurality of historical fault influences and a plurality of historical fault processing schemes, wherein the historical fault types, the historical fault influences and the historical fault processing schemes correspond to the historical abnormal signals. The historical abnormal signal sets may be classified according to a plurality of historical abnormal signal types to obtain a plurality of types of abnormal signal sets. Each abnormal signal set comprises a plurality of historical abnormal signals corresponding to the same historical abnormal signal type, a plurality of historical fault types, a plurality of historical fault influences and a plurality of historical fault processing schemes. The mapping database of anomaly signals to anomaly devices includes sets of anomaly signals of multiple classes. The equipment fault report comprises a fault type, a fault influence and a fault processing scheme of the first electric equipment corresponding to the filtered signal data set. Illustratively, when obtaining the real-time anomaly signal set, a historical data query is performed based on the filtered signal data sets, obtaining a plurality of historical filtered signal data sets, a plurality of historical anomaly signal sets. Based on an isolated forest algorithm, a plurality of historical filtered signal data sets and a plurality of historical abnormal signal sets are continuously self-trained and learned to a convergence state, and a signal abnormal recognition model is obtained. Inputting the filtered signal data set into a signal abnormality recognition model, and detecting abnormal signals of the filtered signal data set through the signal abnormality recognition model to obtain a real-time abnormal signal set. The method achieves the technical effects of obtaining an accurate equipment fault report by carrying out abnormality identification and fault analysis on the filtered signal data set, thereby improving the fault diagnosis quality of the electrical equipment.
In summary, the intelligent fault diagnosis method for the electrical equipment provided by the application has the following technical effects:
1. the method comprises the steps that signal acquisition is conducted on an electric operation loop of first electric equipment through a signal acquisition device, and a loop signal data set is obtained; acquiring an environment signal data set by carrying out signal acquisition on the real-time operation environment of the first electric equipment; generating an environmental signal separation parameter by performing feature recognition on the environmental signal data set; performing signal processing on the loop signal data set according to the environmental signal separation parameters to obtain a filtered signal data set; acquiring a real-time abnormal signal set by carrying out signal abnormality identification on the filtered signal data set; and generating a device fault report by carrying out fault analysis on the real-time abnormal signal set. The intelligent, reliable and accurate fault analysis is performed on the electrical equipment, the fault diagnosis accuracy of the electrical equipment is improved, the fault diagnosis quality of the electrical equipment is improved, and a powerful guarantee technical effect is provided for the normal operation of the electrical equipment.
2. And accurately and efficiently analyzing the voltage configuration data and the magnetic induction configuration data and performing magnetic induction analysis through the signal interference analysis model to obtain a reliable high-voltage interference index, a reliable magnetic induction interference index and a plurality of signal interference indexes, thereby improving the accuracy of fault diagnosis of the electrical equipment.
3. And carrying out environmental noise separation on the loop signal data set through the environmental signal separation parameters to obtain a filtered signal data set, thereby reducing the interference of the environmental noise on the loop signal and improving the reliability and accuracy of fault diagnosis of the electrical equipment.
Example two
Based on the same inventive concept as the intelligent fault diagnosis method of an electrical device in the foregoing embodiment, the present application further provides an intelligent fault diagnosis system of an electrical device, where the system is communicatively connected to a signal acquisition device, referring to fig. 3, and the system includes:
an electric operation circuit acquisition module 11, wherein the electric operation circuit acquisition module 11 is used for acquiring an electric operation circuit according to each component of the first electric equipment;
the loop signal acquisition module 12 is used for acquiring signals of the electric operation loop according to the signal acquisition device to obtain a loop signal data set;
an operation environment obtaining module 13, where the operation environment obtaining module 13 is configured to obtain a real-time operation environment of the first electrical device;
the environment signal acquisition module 14 is configured to acquire an environment signal data set by performing signal acquisition on the real-time operation environment;
the feature recognition module 15 is used for performing feature recognition on the environmental signal data set by the feature recognition module 15 to generate environmental signal separation parameters;
the signal processing module 16, the signal processing module 16 is configured to perform signal processing on the loop signal data set according to the environmental signal separation parameter to obtain a filtered signal data set;
the signal anomaly identification module 17 is used for carrying out signal anomaly identification on the filtered signal data set to obtain a real-time anomaly signal set;
the fault analysis module 18 is configured to generate an equipment fault report by performing fault analysis on the real-time abnormal signal set by the fault analysis module 18.
Further, the system further comprises:
the first execution module is used for acquiring a plurality of electric operation loops according to the connection relation of the constituent components;
the signal interference analysis module is used for carrying out signal interference analysis on the plurality of electric operation loops to obtain a plurality of signal interference indexes;
the identification electric operation loop determining module is used for identifying the plurality of electric operation loops according to the plurality of signal interference indexes to obtain an identification electric operation loop;
the second execution module is used for carrying out signal acquisition on the identification electric operation loop according to the signal acquisition device.
Further, the system further comprises:
the configuration data acquisition module is used for acquiring voltage configuration data and magnetic induction configuration data of the plurality of electric operation loops;
the interference index acquisition module is used for inputting the voltage configuration data and the magnetic induction configuration data into a signal interference analysis model, and acquiring a high-voltage interference index and a magnetic induction interference index according to the signal interference analysis model;
and the weight calculation module is used for carrying out weight calculation on the high-voltage interference index and the magnetic induction interference index to obtain the plurality of signal interference indexes.
Further, the system further comprises:
the high-voltage performance analysis module is used for carrying out high-voltage performance analysis according to the voltage configuration data to obtain a high-voltage interference index, wherein the high-voltage interference index is used for identifying the influence degree of high-voltage intensity on signals output by the component;
the high-voltage electric circuit acquisition module is used for identifying N high-voltage electric circuits larger than a preset high-voltage interference index from the plurality of electric operation circuits according to the high-voltage interference index;
and the third execution module is used for generating a signal sampling identifier based on the N high-voltage electric loops.
Further, the system further comprises:
the magnetic induction analysis module is used for carrying out magnetic induction analysis according to the magnetic induction configuration data to obtain a magnetic induction interference index, wherein the magnetic induction interference index is used for identifying the influence degree of magnetic field induction intensity on signals output by the component;
the magnetic induction electric loop acquisition module is used for identifying and acquiring N magnetic induction electric loops larger than a preset magnetic induction interference index from the plurality of electric operation loops according to the magnetic induction interference index;
and the fourth execution module is used for generating a signal sampling identifier based on the N magnetic induction electric loops.
Further, the system further comprises:
a historical abnormal signal set determination module for collecting a historical abnormal signal set of the first electrical device;
the signal set classification module is used for classifying based on the historical abnormal signal sets to obtain multiple types of abnormal signal sets;
the database generation module is used for generating a mapping database of the abnormal signals and the abnormal devices by using the multi-class abnormal signal set;
and the fault report generation module is used for carrying out fault analysis on the received real-time abnormal signal set according to the mapping database to generate the equipment fault report.
Further, the system further comprises:
the noise identification module is used for carrying out noise identification on the environmental signal data set to acquire a plurality of noise sources;
the noise characteristic acquisition module is used for acquiring noise characteristics by carrying out characteristic analysis on the plurality of noise sources;
the denoising parameter determination module is used for analyzing the noise characteristics based on wavelet transformation and determining denoising pretreatment parameters;
and the environment noise separation module is used for taking the denoising pretreatment parameter as the environment signal separation parameter and carrying out environment noise separation on the loop signal data set.
The intelligent fault diagnosis system for the electrical equipment provided by the embodiment of the application can execute the intelligent fault diagnosis method for the electrical equipment provided by any embodiment of the application, and has the corresponding functional modules and beneficial effects of the execution method.
All the included modules are only divided according to the functional logic, but are not limited to the above-mentioned division, so long as the corresponding functions can be realized; in addition, the specific names of the functional modules are only for distinguishing from each other, and are not used for limiting the protection scope of the present application.
Example III
Fig. 4 is a schematic structural diagram of an electronic device provided in a third embodiment of the present application, and shows a block diagram of an exemplary electronic device suitable for implementing an embodiment of the present application. The electronic device shown in fig. 4 is only an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present application. As shown in fig. 4, the electronic device includes a processor 31, a memory 32, an input device 33, and an output device 34; the number of processors 31 in the electronic device may be one or more, in fig. 4, one processor 31 is taken as an example, and the processors 31, the memory 32, the input device 33 and the output device 34 in the electronic device may be connected by a bus or other means, in fig. 4, by bus connection is taken as an example.
The memory 32 is a computer readable storage medium, and may be used to store a software program, a computer executable program, and a module, such as a program instruction/module corresponding to an intelligent fault diagnosis method for an electrical device in an embodiment of the present application. The processor 31 executes various functional applications of the computer device and data processing by running software programs, instructions and modules stored in the memory 32, i.e., implements the above-described intelligent fault diagnosis method of an electrical device.
The application provides an intelligent fault diagnosis method of electrical equipment, wherein the method is applied to an intelligent fault diagnosis system of the electrical equipment, and the method comprises the following steps: the method comprises the steps that signal acquisition is conducted on an electric operation loop of first electric equipment through a signal acquisition device, and a loop signal data set is obtained; acquiring an environment signal data set by carrying out signal acquisition on the real-time operation environment of the first electric equipment; generating an environmental signal separation parameter by performing feature recognition on the environmental signal data set; performing signal processing on the loop signal data set according to the environmental signal separation parameters to obtain a filtered signal data set; acquiring a real-time abnormal signal set by carrying out signal abnormality identification on the filtered signal data set; and generating a device fault report by carrying out fault analysis on the real-time abnormal signal set. The technical problems that in the prior art, the fault diagnosis accuracy aiming at the electrical equipment is insufficient, and then the fault diagnosis effect of the electrical equipment is poor are solved. The intelligent, reliable and accurate fault analysis is performed on the electrical equipment, the fault diagnosis accuracy of the electrical equipment is improved, the fault diagnosis quality of the electrical equipment is improved, and a powerful guarantee technical effect is provided for the normal operation of the electrical equipment.
Note that the above is only a preferred embodiment of the present application and the technical principle applied. It will be understood by those skilled in the art that the present application is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the application. Therefore, while the application has been described in connection with the above embodiments, the application is not limited to the embodiments, but may be embodied in many other equivalent forms without departing from the spirit or scope of the application, which is set forth in the following claims.
Claims (5)
1. An intelligent fault diagnosis method for an electrical device, wherein the method is applied to an intelligent fault diagnosis system for the electrical device, the system is in communication connection with a signal acquisition device, and the method comprises the following steps:
acquiring a plurality of electric operation loops according to the connection relation of all the constituent components of the first electric equipment;
performing signal interference analysis on the plurality of electric operation loops to obtain a plurality of signal interference indexes;
identifying the plurality of electric operation loops according to the plurality of signal interference indexes to obtain an identified electric operation loop;
the signal acquisition device acquires signals of the identification electric operation loop to obtain a loop signal data set;
acquiring a real-time operation environment of the first electrical equipment;
acquiring an environment signal data set by carrying out signal acquisition on the real-time operation environment;
performing feature recognition on the environmental signal data set to generate environmental signal separation parameters;
performing signal processing on the loop signal data set according to the environmental signal separation parameter to obtain a filtered signal data set;
carrying out signal anomaly identification on the filtered signal data set to obtain a real-time anomaly signal set;
generating a device fault report by performing fault analysis on the real-time abnormal signal set;
the signal interference analysis is performed on the plurality of electric operation loops to obtain a plurality of signal interference indexes, including:
acquiring voltage configuration data and magnetic induction configuration data of the plurality of electric operation loops;
inputting the voltage configuration data and the magnetic induction configuration data into a signal interference analysis model, and acquiring a high-voltage interference index and a magnetic induction interference index according to the signal interference analysis model;
the high-voltage interference indexes and the magnetic induction interference indexes are used for carrying out weight calculation to obtain a plurality of signal interference indexes, wherein when the plurality of signal interference indexes are obtained, weight distribution is carried out on a plurality of high-voltage intensity influence parameters and a plurality of magnetic field induction intensity influence parameters according to preset and determined high-voltage weight parameters and magnetic induction weight parameters to obtain a plurality of high-voltage signal interference indexes and a plurality of magnetic induction signal interference indexes, the plurality of high-voltage signal interference indexes and the plurality of magnetic induction signal interference indexes are output to be a plurality of signal interference indexes, the high-voltage interference indexes comprise a plurality of high-voltage intensity influence parameters corresponding to a plurality of high-voltage electric loops, and the magnetic induction interference indexes comprise a plurality of magnetic field induction intensity influence parameters corresponding to a plurality of magnetic induction electric loops;
wherein the method further comprises:
performing high-voltage performance analysis according to the voltage configuration data to obtain a high-voltage interference index, wherein the high-voltage interference index is used for identifying the influence degree of high-voltage intensity on signals output by the component;
according to the high-voltage interference index, identifying N high-voltage electric circuits larger than a preset high-voltage interference index from the plurality of electric operation circuits;
generating a signal sampling identifier based on the N high-voltage electric loops;
performing magnetic susceptibility analysis according to the magnetic susceptibility configuration data to obtain a magnetic susceptibility disturbance index, wherein the magnetic susceptibility disturbance index is used for identifying the influence degree of magnetic field induction intensity on signals output by the component;
according to the magnetic induction interference index, identifying N magnetic induction electric loops which are larger than a preset magnetic induction interference index from the electric operation loops;
generating a signal sampling identifier based on the N magnetic induction electric loops;
the feature recognition is performed on the environmental signal data set, and environmental signal separation parameters are generated, including:
noise identification is carried out on the environmental signal data set, and a plurality of noise sources are obtained;
acquiring noise characteristics by performing characteristic analysis on the plurality of noise sources;
analyzing the noise characteristics based on wavelet transformation, and determining denoising pretreatment parameters;
and taking the denoising pretreatment parameter as the environmental signal separation parameter, and performing environmental noise separation on the loop signal data set.
2. The method of claim 1, wherein the method further comprises:
collecting a historical anomaly signal set of the first electrical device;
classifying based on the historical abnormal signal sets to obtain multiple types of abnormal signal sets;
generating a mapping database of the abnormal signal and the abnormal equipment by using the multi-class abnormal signal set;
and carrying out fault analysis on the received real-time abnormal signal set according to the mapping database to generate the equipment fault report.
3. An intelligent fault diagnosis system for an electrical device, the system being in communication with a signal acquisition device, the system comprising:
the electric operation circuit acquisition module is used for acquiring a plurality of electric operation circuits according to the connection relation of all the constituent components of the first electric equipment;
the signal interference analysis module is used for carrying out signal interference analysis on the plurality of electric operation loops to obtain a plurality of signal interference indexes;
the identification electric operation loop determining module is used for identifying the plurality of electric operation loops according to the plurality of signal interference indexes to obtain an identification electric operation loop;
the loop signal acquisition module is used for acquiring signals of the identification electric operation loop according to the signal acquisition device to obtain a loop signal data set;
the operation environment acquisition module is used for acquiring the real-time operation environment of the first electrical equipment;
the environment signal acquisition module is used for acquiring an environment signal data set by carrying out signal acquisition on the real-time operation environment;
the characteristic recognition module is used for carrying out characteristic recognition on the environmental signal data set and generating environmental signal separation parameters;
the signal processing module is used for performing signal processing on the loop signal data set according to the environmental signal separation parameter to obtain a filtered signal data set;
the signal abnormality identification module is used for carrying out signal abnormality identification on the filtered signal data set to obtain a real-time abnormal signal set;
the fault analysis module is used for generating a device fault report by carrying out fault analysis on the real-time abnormal signal set;
the configuration data acquisition module is used for acquiring voltage configuration data and magnetic induction configuration data of the plurality of electric operation loops;
the interference index acquisition module is used for inputting the voltage configuration data and the magnetic induction configuration data into a signal interference analysis model, and acquiring a high-voltage interference index and a magnetic induction interference index according to the signal interference analysis model;
the weight calculation module is used for carrying out weight calculation on the high-voltage interference index and the magnetic induction interference index to obtain a plurality of signal interference indexes, wherein when the plurality of signal interference indexes are obtained, weight distribution is carried out on a plurality of high-voltage intensity influence parameters and a plurality of magnetic field induction intensity influence parameters according to preset and determined high-voltage weight parameters and magnetic induction weight parameters to obtain a plurality of high-voltage signal interference indexes and a plurality of magnetic induction signal interference indexes, the plurality of high-voltage signal interference indexes and the plurality of magnetic induction signal interference indexes are output as a plurality of signal interference indexes, the high-voltage interference indexes comprise a plurality of high-voltage intensity influence parameters corresponding to a plurality of high-voltage electric circuits, and the magnetic induction interference indexes comprise a plurality of magnetic field induction intensity influence parameters corresponding to a plurality of magnetic induction electric circuits;
the high-voltage performance analysis module is used for carrying out high-voltage performance analysis according to the voltage configuration data to obtain a high-voltage interference index, wherein the high-voltage interference index is used for identifying the influence degree of high-voltage intensity on signals output by the component;
the high-voltage electric circuit acquisition module is used for identifying N high-voltage electric circuits larger than a preset high-voltage interference index from the plurality of electric operation circuits according to the high-voltage interference index;
the third execution module is used for generating a signal sampling identifier based on the N high-voltage electric loops;
the magnetic induction analysis module is used for carrying out magnetic induction analysis according to the magnetic induction configuration data to obtain a magnetic induction interference index, wherein the magnetic induction interference index is used for identifying the influence degree of magnetic field induction intensity on signals output by the component;
the magnetic induction electric loop acquisition module is used for identifying and acquiring N magnetic induction electric loops larger than a preset magnetic induction interference index from the plurality of electric operation loops according to the magnetic induction interference index;
the fourth execution module is used for generating a signal sampling identifier based on the N magnetic induction electric loops;
the noise identification module is used for carrying out noise identification on the environmental signal data set to acquire a plurality of noise sources;
the noise characteristic acquisition module is used for acquiring noise characteristics by carrying out characteristic analysis on the plurality of noise sources;
the denoising parameter determination module is used for analyzing the noise characteristics based on wavelet transformation and determining denoising pretreatment parameters;
and the environment noise separation module is used for taking the denoising pretreatment parameter as the environment signal separation parameter and carrying out environment noise separation on the loop signal data set.
4. An electronic device, the electronic device comprising:
a memory for storing executable instructions;
a processor for implementing a method for intelligent fault diagnosis of an electrical apparatus according to any one of claims 1 to 2 when executing executable instructions stored in said memory.
5. A computer-readable storage medium, on which a computer program is stored, characterized in that the program, when executed by a processor, implements a method for intelligent fault diagnosis of an electrical apparatus according to any one of claims 1 to 2.
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Citations (26)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| GB2087573A (en) * | 1980-11-17 | 1982-05-26 | Gec General Signal Ltd | Line signal fault detection system |
| US5070468A (en) * | 1988-07-20 | 1991-12-03 | Mitsubishi Jukogyo Kabushiki Kaisha | Plant fault diagnosis system |
| JP2009162703A (en) * | 2008-01-10 | 2009-07-23 | Toshiba Corp | Method and system for fault diagnosis of electronic component |
| CN102867132A (en) * | 2012-10-16 | 2013-01-09 | 南京航空航天大学 | Aviation direct-current converter online fault combined prediction method based on fractional order wavelet transformation |
| CN103048569A (en) * | 2012-12-24 | 2013-04-17 | 北京航空航天大学 | Optimization method for electromagnetic compatibility fault repair by adopting electromagnetic compatibility test over-standard strategy |
| CN104571099A (en) * | 2015-01-26 | 2015-04-29 | 北京国能日新系统控制技术有限公司 | Photovoltaic fault diagnosis system and method based on theoretical calculation and data analysis |
| CN104849628A (en) * | 2015-04-24 | 2015-08-19 | 国网四川省电力公司电力科学研究院 | Fault analysis detection method for cable accessory |
| CN106920042A (en) * | 2017-03-03 | 2017-07-04 | 云南电网有限责任公司电力科学研究院 | A kind of transmission line of electricity abnormality space-time multi dimensional analysis method |
| CN106990321A (en) * | 2017-03-22 | 2017-07-28 | 武汉双微电气股份有限公司 | Distribution line failure identifying device based on voltage monitoring |
| CN207611097U (en) * | 2017-11-13 | 2018-07-13 | 广西电网有限责任公司电力科学研究院 | A kind of multiplexing electric abnormality field monitoring early warning system |
| CN110135088A (en) * | 2019-05-20 | 2019-08-16 | 哈尔滨工业大学 | Analog circuit incipient fault detection method based on the normal envelope model of degenerative character parameter |
| CN110716155A (en) * | 2019-10-24 | 2020-01-21 | 国网上海市电力公司 | Low-voltage power supply loop abnormal working condition perception algorithm and device |
| CN110988587A (en) * | 2019-11-13 | 2020-04-10 | 上海恒能泰企业管理有限公司 | Distribution network anomaly detection method based on maximum and minimum characteristic value method |
| CN111398730A (en) * | 2020-03-01 | 2020-07-10 | 华中科技大学 | Power distribution network based on passive injection direct current signal and fault identification method thereof |
| CN111523718A (en) * | 2020-04-16 | 2020-08-11 | 广东电网有限责任公司 | Line fault prediction method, system and storage medium based on artificial intelligence |
| CN111679176A (en) * | 2020-06-23 | 2020-09-18 | 缙云县曦田节能科技有限公司 | Circuit fault monitoring system for power saving |
| CN112213577A (en) * | 2020-09-21 | 2021-01-12 | 长园共创电力安全技术股份有限公司 | Method and device for detecting electrical operation, computer key and storage medium |
| CN112255538A (en) * | 2020-10-14 | 2021-01-22 | 国网青海省电力公司海西供电公司 | Multi-feature optimization fusion high-voltage circuit breaker fault diagnosis method |
| CN113759215A (en) * | 2021-10-18 | 2021-12-07 | 南京乐帆电气科技有限公司 | Power failure detection system and method based on Internet of things |
| CN114264945A (en) * | 2021-12-13 | 2022-04-01 | 广东电网有限责任公司 | Equipment state detection method and device |
| CN115327282A (en) * | 2022-09-13 | 2022-11-11 | 恒华数字科技集团有限公司 | Power supply system fault diagnosis method |
| CN115343573A (en) * | 2022-07-29 | 2022-11-15 | 南京邮电大学 | Voiceprint model evolution equipment fault fusion judgment method facing power grid monitoring |
| CN115453236A (en) * | 2022-08-24 | 2022-12-09 | 大连海事大学 | Fault diagnosis and health assessment method for ship wind wing power system |
| CN115494356A (en) * | 2022-09-20 | 2022-12-20 | 江苏方天电力技术有限公司 | Intelligent online monitoring system and method for partial discharge of power equipment |
| CN115586402A (en) * | 2022-12-09 | 2023-01-10 | 安徽中鑫继远信息技术股份有限公司 | Power distribution network fault diagnosis and processing method |
| CN115833400A (en) * | 2023-02-07 | 2023-03-21 | 山东盛日电力集团有限公司 | Monitoring and early warning method and system for power equipment of transformer substation |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| GB201505082D0 (en) * | 2015-03-25 | 2015-05-06 | Optasense Holdings Ltd | Detecting failure locations in power cables |
-
2023
- 2023-03-23 CN CN202310286162.5A patent/CN115993504B/en active Active
Patent Citations (26)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| GB2087573A (en) * | 1980-11-17 | 1982-05-26 | Gec General Signal Ltd | Line signal fault detection system |
| US5070468A (en) * | 1988-07-20 | 1991-12-03 | Mitsubishi Jukogyo Kabushiki Kaisha | Plant fault diagnosis system |
| JP2009162703A (en) * | 2008-01-10 | 2009-07-23 | Toshiba Corp | Method and system for fault diagnosis of electronic component |
| CN102867132A (en) * | 2012-10-16 | 2013-01-09 | 南京航空航天大学 | Aviation direct-current converter online fault combined prediction method based on fractional order wavelet transformation |
| CN103048569A (en) * | 2012-12-24 | 2013-04-17 | 北京航空航天大学 | Optimization method for electromagnetic compatibility fault repair by adopting electromagnetic compatibility test over-standard strategy |
| CN104571099A (en) * | 2015-01-26 | 2015-04-29 | 北京国能日新系统控制技术有限公司 | Photovoltaic fault diagnosis system and method based on theoretical calculation and data analysis |
| CN104849628A (en) * | 2015-04-24 | 2015-08-19 | 国网四川省电力公司电力科学研究院 | Fault analysis detection method for cable accessory |
| CN106920042A (en) * | 2017-03-03 | 2017-07-04 | 云南电网有限责任公司电力科学研究院 | A kind of transmission line of electricity abnormality space-time multi dimensional analysis method |
| CN106990321A (en) * | 2017-03-22 | 2017-07-28 | 武汉双微电气股份有限公司 | Distribution line failure identifying device based on voltage monitoring |
| CN207611097U (en) * | 2017-11-13 | 2018-07-13 | 广西电网有限责任公司电力科学研究院 | A kind of multiplexing electric abnormality field monitoring early warning system |
| CN110135088A (en) * | 2019-05-20 | 2019-08-16 | 哈尔滨工业大学 | Analog circuit incipient fault detection method based on the normal envelope model of degenerative character parameter |
| CN110716155A (en) * | 2019-10-24 | 2020-01-21 | 国网上海市电力公司 | Low-voltage power supply loop abnormal working condition perception algorithm and device |
| CN110988587A (en) * | 2019-11-13 | 2020-04-10 | 上海恒能泰企业管理有限公司 | Distribution network anomaly detection method based on maximum and minimum characteristic value method |
| CN111398730A (en) * | 2020-03-01 | 2020-07-10 | 华中科技大学 | Power distribution network based on passive injection direct current signal and fault identification method thereof |
| CN111523718A (en) * | 2020-04-16 | 2020-08-11 | 广东电网有限责任公司 | Line fault prediction method, system and storage medium based on artificial intelligence |
| CN111679176A (en) * | 2020-06-23 | 2020-09-18 | 缙云县曦田节能科技有限公司 | Circuit fault monitoring system for power saving |
| CN112213577A (en) * | 2020-09-21 | 2021-01-12 | 长园共创电力安全技术股份有限公司 | Method and device for detecting electrical operation, computer key and storage medium |
| CN112255538A (en) * | 2020-10-14 | 2021-01-22 | 国网青海省电力公司海西供电公司 | Multi-feature optimization fusion high-voltage circuit breaker fault diagnosis method |
| CN113759215A (en) * | 2021-10-18 | 2021-12-07 | 南京乐帆电气科技有限公司 | Power failure detection system and method based on Internet of things |
| CN114264945A (en) * | 2021-12-13 | 2022-04-01 | 广东电网有限责任公司 | Equipment state detection method and device |
| CN115343573A (en) * | 2022-07-29 | 2022-11-15 | 南京邮电大学 | Voiceprint model evolution equipment fault fusion judgment method facing power grid monitoring |
| CN115453236A (en) * | 2022-08-24 | 2022-12-09 | 大连海事大学 | Fault diagnosis and health assessment method for ship wind wing power system |
| CN115327282A (en) * | 2022-09-13 | 2022-11-11 | 恒华数字科技集团有限公司 | Power supply system fault diagnosis method |
| CN115494356A (en) * | 2022-09-20 | 2022-12-20 | 江苏方天电力技术有限公司 | Intelligent online monitoring system and method for partial discharge of power equipment |
| CN115586402A (en) * | 2022-12-09 | 2023-01-10 | 安徽中鑫继远信息技术股份有限公司 | Power distribution network fault diagnosis and processing method |
| CN115833400A (en) * | 2023-02-07 | 2023-03-21 | 山东盛日电力集团有限公司 | Monitoring and early warning method and system for power equipment of transformer substation |
Non-Patent Citations (1)
| Title |
|---|
| 变电站二次回路抗干扰分析;孙磊;;山东工业技术(第02期);全文 * |
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