CN112598537B - Power equipment fault diagnosis method and device and terminal equipment - Google Patents
Power equipment fault diagnosis method and device and terminal equipment Download PDFInfo
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- CN112598537B CN112598537B CN202011530954.5A CN202011530954A CN112598537B CN 112598537 B CN112598537 B CN 112598537B CN 202011530954 A CN202011530954 A CN 202011530954A CN 112598537 B CN112598537 B CN 112598537B
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/06—Electricity, gas or water supply
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- G01—MEASURING; TESTING
- G01H—MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
- G01H17/00—Measuring mechanical vibrations or ultrasonic, sonic or infrasonic waves, not provided for in the preceding groups
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
Abstract
The invention provides a method, a device and a terminal device for diagnosing faults of power equipment, wherein the method comprises the following steps: acquiring audio data corresponding to target power equipment, and performing feature extraction on the audio data to obtain a fusion feature vector; acquiring image data corresponding to target electric power equipment, and determining the equipment type of the target electric power equipment according to the image data; if the target power equipment is a type of equipment, inputting the fusion characteristic vector into a preset first fault diagnosis model to obtain a fault diagnosis result of the target power equipment; and if the target power equipment is the second type of equipment, inputting the fusion characteristic vector into a preset second fault diagnosis model to obtain a fault diagnosis result of the target power equipment. The method, the device and the terminal equipment for diagnosing the fault of the power equipment can ensure the fault diagnosis precision of the power equipment and improve the diagnosis efficiency of the power equipment.
Description
Technical Field
The invention belongs to the technical field of fault diagnosis, and particularly relates to a method and a device for diagnosing faults of power equipment and terminal equipment.
Background
The structure of the power equipment is complex, and faults are easy to occur during operation, so that the fault monitoring needs to be carried out on the power equipment, and the safe operation of the power equipment is maintained. The traditional power equipment fault detection modes comprise professional worker experience detection, stop operation test detection and the like, but the modes are high in labor cost and low in efficiency, and normal operation of a power system is easily influenced. However, when the power equipment is in overload operation, circuit fault, mechanical fault and the like, audio signals measured at different positions of the power equipment contain rich information, so that fault diagnosis technology based on the audio signals is developed.
However, when the number of the electric power devices increases, the amount of data included in the audio signal of the electric power device also increases greatly, so that the conventional fault diagnosis method based on the audio signal has low diagnosis efficiency, and the improvement of the diagnosis efficiency sacrifices the diagnosis accuracy. Therefore, how to improve the diagnosis efficiency of the power equipment while ensuring the fault diagnosis accuracy of the power equipment becomes a technical problem that needs to be solved urgently by the person skilled in the art.
Disclosure of Invention
The invention aims to provide a method and a device for diagnosing faults of electric equipment and terminal equipment, so that the diagnosis efficiency of the electric equipment is improved while the fault diagnosis precision of the electric equipment is ensured.
In a first aspect of the embodiments of the present invention, a method for diagnosing a fault of an electrical device is provided, including:
acquiring audio data corresponding to target power equipment, and performing feature extraction on the audio data to obtain a fusion feature vector; acquiring image data corresponding to target electric power equipment, and determining the equipment type of the target electric power equipment according to the image data;
if the target power equipment is a type of equipment, inputting the fusion characteristic vector into a preset first fault diagnosis model to obtain a fault diagnosis result of the target power equipment; if the target power equipment is the second type of equipment, inputting the fusion characteristic vector into a preset second fault diagnosis model to obtain a fault diagnosis result of the target power equipment;
the diagnosis precision of the first fault diagnosis model is smaller than that of the second fault diagnosis model, and the diagnosis efficiency of the first fault diagnosis model is larger than that of the second fault diagnosis module.
In a second aspect of the embodiments of the present invention, there is provided a power equipment fault diagnosis apparatus, including:
the data acquisition module is used for acquiring audio data corresponding to the target power equipment and extracting the characteristics of the audio data to obtain a fusion characteristic vector; acquiring image data corresponding to target electric power equipment, and determining the equipment type of the target electric power equipment according to the image data;
the fault diagnosis module is used for inputting the fusion feature vector into a preset first fault diagnosis model to obtain a fault diagnosis result of the target power equipment when the target power equipment is one type of equipment; when the target electric power equipment is the second-class equipment, inputting the fusion characteristic vector into a preset second fault diagnosis model to obtain a fault diagnosis result of the target electric power equipment;
the diagnosis precision of the first fault diagnosis model is smaller than that of the second fault diagnosis model, and the diagnosis efficiency of the first fault diagnosis model is larger than that of the second fault diagnosis module.
In a third aspect of the embodiments of the present invention, there is provided a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the steps of the power device fault diagnosis method when executing the computer program.
In a fourth aspect of the embodiments of the present invention, a computer-readable storage medium is provided, which stores a computer program, and the computer program, when executed by a processor, implements the steps of the power equipment fault diagnosis method described above.
The method, the device and the terminal equipment for diagnosing the fault of the power equipment have the advantages that: different from the prior art, the method and the device for diagnosing the fault of the target power equipment have the advantages that the image data of the target power equipment is acquired while the audio data is acquired, the equipment type of the target power equipment is judged based on the image data, and the fault diagnosis model can be flexibly selected according to the equipment type of the target power equipment as long as the judgment standard of the equipment type of the target power equipment is preset, so that the fault diagnosis precision of the target power equipment is ensured, and the fault diagnosis efficiency of the target power equipment is improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed for the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
Fig. 1 is a schematic flow chart of a fault diagnosis method for power equipment according to an embodiment of the present invention;
fig. 2 is a block diagram of a power equipment fault diagnosis apparatus according to an embodiment of the present invention;
fig. 3 is a schematic block diagram of a terminal device according to an embodiment of the present invention.
Detailed Description
In order to make the technical problems, technical solutions and advantageous effects to be solved by the present invention more clearly apparent, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Referring to fig. 1, fig. 1 is a schematic flow chart of a power equipment fault diagnosis method according to an embodiment of the present invention, where the method includes:
s101: and acquiring audio data corresponding to the target power equipment, and performing feature extraction on the audio data to obtain a fusion feature vector. The method comprises the steps of obtaining image data corresponding to target electric power equipment, and determining the equipment type of the target electric power equipment according to the image data.
In this embodiment, the target electrical device may be an electrical primary device or an electrical secondary device, specifically, the target electrical device may be a motor or other mechanical component capable of emitting an audio signal, and is not limited herein.
In this embodiment, determining the device type of the target power device according to the image data may be detailed as:
and matching the image data with the image data in the preset database to determine the equipment identification of the target electrical equipment.
And determining the device type of the target power device based on the device identifier and a preset mapping relation table. The mapping relation table is a corresponding relation between the equipment identifier of each electric power equipment and the equipment type of the electric power equipment.
S102: and if the target power equipment is one type of equipment, inputting the fusion characteristic vector into a preset first fault diagnosis model to obtain a fault diagnosis result of the target power equipment. And if the target power equipment is the second type of equipment, inputting the fusion characteristic vector into a preset second fault diagnosis model to obtain a fault diagnosis result of the target power equipment.
In the present embodiment, the diagnosis accuracy of the first fault diagnosis model is smaller than that of the second fault diagnosis model, and the diagnosis efficiency of the first fault diagnosis model is greater than that of the second fault diagnosis module.
In this embodiment, the first-class device may be a device whose aging degree is greater than a preset threshold or whose failure frequency is greater than a preset frequency, and the second-class device may be a device whose aging degree is not greater than a preset threshold and whose failure frequency is not greater than a preset frequency. That is to say, the probability of a type of equipment failing is high, and the failure samples are rich, in this case, the failure diagnosis can be performed based on the failure diagnosis model with high diagnosis efficiency and low diagnosis precision, so that the diagnosis efficiency is improved. The probability of the second-class equipment having faults is low, the fault sample is short, and fault diagnosis can be performed based on a fault diagnosis model with low diagnosis efficiency and high diagnosis precision, so that the diagnosis precision is ensured.
The method and the device have the advantages that the method and the device are different from the prior art, the image data of the target power equipment is acquired while the audio data is acquired, the equipment type of the target power equipment is judged based on the image data, and the fault diagnosis model can be flexibly selected according to the equipment type of the target power equipment as long as the judgment standard of the equipment type of the target power equipment is preset, so that the fault diagnosis precision of the target power equipment is ensured, and the fault diagnosis efficiency of the target power equipment is improved.
Optionally, as a specific implementation manner of the power equipment fault diagnosis method provided in the embodiment of the present invention, if the target power equipment is a second type of equipment, after the fused feature vector is input into a preset second fault diagnosis model and a fault diagnosis result of the target power equipment is obtained, the power equipment fault diagnosis method may further include:
and recording the failure frequency of the target power equipment in a preset time period.
And if the failure frequency of the target power equipment is greater than the preset failure frequency, changing the equipment type of the target power equipment into one type of equipment.
In this embodiment, the device type of the target electrical device may be updated based on each failure diagnosis result, and if the failure frequency of the target electrical device is greater than the preset failure frequency, the device type of the target electrical device is changed to a class of device. Specifically, the device type corresponding to the device identifier of the target electrical device in the mapping relationship table may be changed into a class of device. Optionally, after the device type of the target electrical device is changed to a type of device, the number of failures of the target electrical device may be cleared, and counting may be restarted.
Optionally, as a specific implementation manner of the power equipment fault diagnosis method provided in the embodiment of the present invention, if the target power equipment is a type of equipment, after the fused feature vector is input into a preset first fault diagnosis model and a fault diagnosis result of the target power equipment is obtained, the power equipment fault diagnosis method may further include:
and recording the failure times of the target power equipment in a preset time period.
And if the failure frequency of the target electric power equipment is less than the preset failure frequency, changing the equipment type of the target electric power equipment into second-type equipment.
In this embodiment, the device type of the target electrical device may be updated based on each failure diagnosis result, and if the failure frequency of the target electrical device is less than the preset failure frequency, the device type of the target electrical device is changed to a second type device. Specifically, the device type corresponding to the device identifier of the target electrical device in the mapping relationship table may be changed to a device of type two.
That is, after a certain maintenance, the number of failures of the target electrical device within a preset time period is less than the preset number of failures, and the device type of the target electrical device is updated to the second type of device.
If the target power equipment is newly installed and no fault occurs, the equipment type of the target power equipment is directly set as the second type of equipment.
Optionally, as a specific implementation manner of the power equipment fault diagnosis method provided in the embodiment of the present invention, the performing feature extraction on the audio data to obtain a fusion feature vector includes:
and respectively extracting m-dimensional time-frequency domain parameters and n-dimensional Mel cepstrum coefficients from the audio data to obtain m + n-dimensional fusion feature vectors.
Wherein m and n are integers greater than zero.
Optionally, as a specific implementation manner of the power equipment fault diagnosis method provided by the embodiment of the present invention, both the first fault diagnosis model and the second fault diagnosis model are neural network models.
The number of convolution layers of the first fault diagnosis model is smaller than that of the second fault diagnosis model.
Optionally, as a specific implementation manner of the power equipment fault diagnosis method provided in the embodiment of the present invention, the training method of the first fault diagnosis model is as follows:
s1: obtaining historical sample data corresponding to the target power equipment. The historical sample data contains historical audio data and historical fault results.
S2: and inputting historical audio data into the initial neural network model to obtain a theoretical fault result, and determining a model error based on the theoretical fault result and the historical fault result.
S3: and if the model error is smaller than the preset error, determining that the training of the first fault diagnosis model is finished. And if the model error is not less than the preset error, updating the weight coefficient of each convolution layer in the initial neural network model according to a preset rule, and returning to execute the step S2.
In this embodiment, the training process of the second fault diagnosis model is the same as the training process of the first fault diagnosis model, and is not described herein again.
In this embodiment, the first fault diagnosis model and the second fault diagnosis model may be trained twice at preset time intervals according to the latest sample data, so as to improve the model accuracy.
The method for carrying out secondary training comprises the following steps: and updating the weight coefficient of the original fault diagnosis model by using the newly added sample.
Fig. 2 is a block diagram of a power equipment fault diagnosis apparatus according to an embodiment of the present invention, which corresponds to the power equipment fault diagnosis method according to the above embodiment. For convenience of explanation, only portions related to the embodiments of the present invention are shown. Referring to fig. 2, the power equipment failure diagnosis apparatus 20 includes: a data acquisition module 21 and a fault diagnosis module 22.
The data obtaining module 21 is configured to obtain audio data corresponding to the target power device, and perform feature extraction on the audio data to obtain a fusion feature vector. The method comprises the steps of obtaining image data corresponding to target electric power equipment, and determining the equipment type of the target electric power equipment according to the image data.
And the fault diagnosis module 22 is configured to, when the target electrical device is a type of device, input the fusion feature vector into a preset first fault diagnosis model to obtain a fault diagnosis result of the target electrical device. And when the target power equipment is the second-class equipment, inputting the fusion characteristic vector into a preset second fault diagnosis model to obtain a fault diagnosis result of the target power equipment.
The diagnosis precision of the first fault diagnosis model is smaller than that of the second fault diagnosis model, and the diagnosis efficiency of the first fault diagnosis model is larger than that of the second fault diagnosis module.
Optionally, as a specific implementation manner of the power equipment fault diagnosis apparatus provided in the embodiment of the present invention, the fault diagnosis module 22 is further configured to, when the target power equipment is a second type of equipment, after inputting the fusion feature vector into a preset second fault diagnosis model to obtain a fault diagnosis result of the target power equipment, execute the following steps:
and recording the failure times of the target power equipment in a preset time period.
And if the failure frequency of the target power equipment is greater than the preset failure frequency, changing the equipment type of the target power equipment into one type of equipment.
Optionally, as a specific implementation manner of the power equipment fault diagnosis apparatus provided in the embodiment of the present invention, the fault diagnosis module 22 is further configured to, when the target power equipment is a type of equipment, input the fusion feature vector into a preset first fault diagnosis model to obtain a fault diagnosis result of the target power equipment, execute the following steps:
and recording the failure times of the target power equipment in a preset time period.
And if the failure frequency of the target electric power equipment is less than the preset failure frequency, changing the equipment type of the target electric power equipment into second-type equipment.
Optionally, as a specific implementation manner of the power equipment fault diagnosis apparatus provided in the embodiment of the present invention, determining the equipment type of the target power equipment according to the image data includes:
and matching the image data with the image data in the preset database to determine the equipment identification of the target electrical equipment.
And determining the device type of the target power device based on the device identifier and a preset mapping relation table. The mapping relation table is a corresponding relation between the equipment identifier of each electric power equipment and the equipment type of the electric power equipment.
Optionally, as a specific implementation manner of the power equipment fault diagnosis apparatus provided in the embodiment of the present invention, the performing feature extraction on the audio data to obtain a fusion feature vector includes:
and respectively extracting m-dimensional time-frequency domain parameters and n-dimensional Mel cepstrum coefficients from the audio data to obtain m + n-dimensional fusion feature vectors.
Optionally, as a specific implementation manner of the power equipment fault diagnosis apparatus provided in the embodiment of the present invention, both the first fault diagnosis model and the second fault diagnosis model are neural network models.
The number of convolution layers of the first fault diagnosis model is smaller than that of the second fault diagnosis model.
Optionally, as a specific implementation manner of the power equipment fault diagnosis apparatus provided in the embodiment of the present invention, the power equipment fault diagnosis apparatus may further include a model training module 23, where the model training module 23 is configured to train the first fault diagnosis model and the second fault diagnosis model;
the training method of the first fault diagnosis model comprises the following steps:
s1: obtaining historical sample data corresponding to the target power equipment. The historical sample data contains historical audio data and historical fault results.
S2: and inputting historical audio data into the initial neural network model to obtain a theoretical fault result, and determining a model error based on the theoretical fault result and the historical fault result.
S3: and if the model error is smaller than the preset error, determining that the training of the first fault diagnosis model is finished. And if the model error is not less than the preset error, updating the weight coefficient of each convolution layer in the initial neural network model according to a preset rule, and returning to execute the step S2.
Referring to fig. 3, fig. 3 is a schematic block diagram of a terminal device according to an embodiment of the present invention. The terminal 300 in the present embodiment as shown in fig. 3 may include: one or more processors 301, one or more input devices 302, one or more output devices 303, and one or more memories 304. The processor 301, the input device 302, the output device 303 and the memory 304 are all in communication with each other via a communication bus 305. The memory 304 is used to store computer programs, which include program instructions. Processor 301 is operative to execute program instructions stored in memory 304. Wherein the processor 301 is configured to call program instructions to perform the following functions of operating the modules/units in the above-described device embodiments, such as the functions of the modules 21 to 23 shown in fig. 2.
It should be understood that, in the embodiment of the present invention, the Processor 301 may be a Central Processing Unit (CPU), and the Processor may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, and the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The input device 302 may include a touch pad, a fingerprint sensor (for collecting fingerprint information of a user and direction information of the fingerprint), a microphone, etc., and the output device 303 may include a display (LCD, etc.), a speaker, etc.
The memory 304 may include a read-only memory and a random access memory, and provides instructions and data to the processor 301. A portion of the memory 304 may also include non-volatile random access memory. For example, the memory 304 may also store device type information.
In specific implementation, the processor 301, the input device 302, and the output device 303 described in this embodiment of the present invention may execute the implementation manners described in the first embodiment and the second embodiment of the power device fault diagnosis method provided in this embodiment of the present invention, and may also execute the implementation manners of the terminal described in this embodiment of the present invention, which is not described herein again.
In another embodiment of the present invention, a computer-readable storage medium is provided, in which a computer program is stored, where the computer program includes program instructions, and the program instructions, when executed by a processor, implement all or part of the processes in the method of the above embodiments, and may also be implemented by a computer program instructing associated hardware, and the computer program may be stored in a computer-readable storage medium, and the computer program, when executed by a processor, may implement the steps of the above methods embodiments. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying computer program code, recording medium, U.S. disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution media, and the like. It should be noted that the computer readable medium may include any suitable increase or decrease as required by legislation and patent practice in the jurisdiction, for example, in some jurisdictions, computer readable media may not include electrical carrier signals and telecommunications signals in accordance with legislation and patent practice.
The computer readable storage medium may be an internal storage unit of the terminal of any of the foregoing embodiments, for example, a hard disk or a memory of the terminal. The computer readable storage medium may also be an external storage device of the terminal, such as a plug-in hard disk provided on the terminal, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like. Further, the computer-readable storage medium may also include both an internal storage unit and an external storage device of the terminal. The computer-readable storage medium is used for storing a computer program and other programs and data required by the terminal. The computer-readable storage medium may also be used to temporarily store data that has been output or is to be output.
Those of ordinary skill in the art will appreciate that the elements and algorithm steps of the examples described in connection with the embodiments disclosed herein may be embodied in electronic hardware, computer software, or combinations of both, and that the components and steps of the examples have been described in a functional general in the foregoing description for the purpose of illustrating clearly the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the terminal and the unit described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed terminal and method can be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, a division of a unit is merely a logical division, and an actual implementation may have another division, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces or units, and may also be an electrical, mechanical or other form of connection.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one position, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment of the present invention.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
While the invention has been described with reference to specific embodiments, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A method of diagnosing a fault in an electrical device, comprising:
acquiring audio data corresponding to target power equipment, and performing feature extraction on the audio data to obtain a fusion feature vector; acquiring image data corresponding to target electric power equipment, and determining the equipment type of the target electric power equipment according to the image data;
if the target power equipment is a type of equipment, inputting the fusion characteristic vector into a preset first fault diagnosis model to obtain a fault diagnosis result of the target power equipment; if the target power equipment is the second type of equipment, inputting the fusion characteristic vector into a preset second fault diagnosis model to obtain a fault diagnosis result of the target power equipment;
the diagnosis precision of the first fault diagnosis model is smaller than that of the second fault diagnosis model, and the diagnosis efficiency of the first fault diagnosis model is larger than that of the second fault diagnosis module.
2. The power equipment fault diagnosis method according to claim 1, wherein if the target power equipment is a second type of equipment, after the fused feature vector is input into a preset second fault diagnosis model to obtain a fault diagnosis result of the target power equipment, the method further comprises:
recording the failure times of the target power equipment in a preset time period;
and if the failure times of the target power equipment are larger than the preset failure times, changing the equipment type of the target power equipment into one type of equipment.
3. The power equipment fault diagnosis method according to claim 1, wherein if the target power equipment is a class of equipment, after the fused feature vector is input into a preset first fault diagnosis model to obtain a fault diagnosis result of the target power equipment, the method further comprises:
recording the failure times of the target power equipment in a preset time period;
and if the failure frequency of the target electric power equipment is less than the preset failure frequency, changing the equipment type of the target electric power equipment into second-type equipment.
4. The power equipment fault diagnosis method according to claim 1, wherein the determining of the equipment type of the target power equipment from the image data includes:
matching the image data with image data in a preset database to determine the equipment identification of the target electrical equipment;
determining the device type of the target power device based on the device identifier and a preset mapping relation table; the mapping relation table is the corresponding relation between the equipment identification and the equipment type of each electric power equipment.
5. The power equipment fault diagnosis method according to claim 1, wherein the extracting the features of the audio data to obtain a fused feature vector comprises:
and respectively extracting m-dimensional time-frequency domain parameters and n-dimensional Mel cepstrum coefficients from the audio data to obtain m + n-dimensional fusion feature vectors.
6. The power equipment fault diagnosis method according to claim 1, wherein the first fault diagnosis model and the second fault diagnosis model are both neural network models;
the number of the convolution layers of the first fault diagnosis model is smaller than that of the convolution layers of the second fault diagnosis model.
7. The power equipment fault diagnosis method according to claim 1, characterized in that the training method of the first fault diagnosis model is:
s1: acquiring historical sample data corresponding to target power equipment; the historical sample data comprises historical audio data and historical fault results;
s2: inputting the historical audio data into an initial neural network model to obtain a theoretical fault result, and determining a model error based on the theoretical fault result and the historical fault result;
s3: if the model error is smaller than a preset error, determining that the training of the first fault diagnosis model is finished; and if the model error is not smaller than the preset error, updating the weight coefficient of each convolution layer in the initial neural network model according to a preset rule, and returning to execute the step S2.
8. An electrical equipment fault diagnosis device characterized by comprising:
the data acquisition module is used for acquiring audio data corresponding to the target power equipment and extracting the characteristics of the audio data to obtain a fusion characteristic vector; acquiring image data corresponding to target electric power equipment, and determining the equipment type of the target electric power equipment according to the image data;
the fault diagnosis module is used for inputting the fusion feature vector into a preset first fault diagnosis model to obtain a fault diagnosis result of the target power equipment when the target power equipment is one type of equipment; when the target electric power equipment is the second-class equipment, inputting the fusion characteristic vector into a preset second fault diagnosis model to obtain a fault diagnosis result of the target electric power equipment;
the diagnosis precision of the first fault diagnosis model is smaller than that of the second fault diagnosis model, and the diagnosis efficiency of the first fault diagnosis model is larger than that of the second fault diagnosis module.
9. A terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.
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