CN113670434B - Method and device for identifying sound abnormality of substation equipment and computer equipment - Google Patents
Method and device for identifying sound abnormality of substation equipment and computer equipment Download PDFInfo
- Publication number
- CN113670434B CN113670434B CN202110686609.9A CN202110686609A CN113670434B CN 113670434 B CN113670434 B CN 113670434B CN 202110686609 A CN202110686609 A CN 202110686609A CN 113670434 B CN113670434 B CN 113670434B
- Authority
- CN
- China
- Prior art keywords
- equipment
- sound
- monitored equipment
- model
- monitored
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 40
- 230000005856 abnormality Effects 0.000 title claims description 18
- 238000004590 computer program Methods 0.000 claims description 26
- 230000002159 abnormal effect Effects 0.000 claims description 19
- 238000000605 extraction Methods 0.000 claims description 18
- 239000011159 matrix material Substances 0.000 claims description 9
- 230000000007 visual effect Effects 0.000 claims description 8
- 238000001228 spectrum Methods 0.000 claims 2
- 238000007781 pre-processing Methods 0.000 claims 1
- 238000012544 monitoring process Methods 0.000 abstract description 19
- 238000012423 maintenance Methods 0.000 description 6
- 238000010586 diagram Methods 0.000 description 5
- 238000004891 communication Methods 0.000 description 4
- 238000001514 detection method Methods 0.000 description 4
- 230000008569 process Effects 0.000 description 3
- 238000012800 visualization Methods 0.000 description 3
- 230000005540 biological transmission Effects 0.000 description 2
- 238000010276 construction Methods 0.000 description 2
- 230000004044 response Effects 0.000 description 2
- 230000003068 static effect Effects 0.000 description 2
- 230000008859 change Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000005611 electricity Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 238000009432 framing Methods 0.000 description 1
- 239000004973 liquid crystal related substance Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- 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
-
- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
- G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
- G10L25/03—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 characterised by the type of extracted parameters
-
- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
- G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
- G10L25/48—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 specially adapted for particular use
- G10L25/51—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 specially adapted for particular use for comparison or discrimination
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Computational Linguistics (AREA)
- Signal Processing (AREA)
- Health & Medical Sciences (AREA)
- Audiology, Speech & Language Pathology (AREA)
- Human Computer Interaction (AREA)
- Acoustics & Sound (AREA)
- Multimedia (AREA)
- General Physics & Mathematics (AREA)
- Remote Monitoring And Control Of Power-Distribution Networks (AREA)
Abstract
The application relates to a method and a device for identifying voice anomalies of substation equipment and computer equipment. The method comprises the following steps: acquiring running sound data of monitored equipment in a transformer substation in real time; extracting characteristics of the operation sound data to obtain a sound model to be distinguished for representing the operation state of the monitored equipment; performing fault discrimination on the sound model to be discriminated and a normal sound model for representing normal operation of the monitored equipment through the fault discrimination model, and outputting the offset of the sound model to be discriminated; and determining the running state of the monitored equipment according to the offset. By adopting the method, the efficiency and the effectiveness of fault monitoring of the substation equipment can be improved.
Description
Technical Field
The application relates to the technical field of substation equipment, in particular to a method and a device for identifying abnormality of substation equipment and computer equipment.
Background
With the rapid development of society and economy, the demand of urban electricity is also larger and larger, the load of power equipment is also larger and larger, and once the power equipment fails and stops, great inconvenience and even loss are brought to surrounding residents and enterprises. In the practical application process, the existing equipment fault monitoring of the transformer substation, including basic working parameter monitoring and vibration-based fault monitoring, can be found, and the condition of missed detection or incapability of alarming in time can be caused.
However, in the actual substation equipment detection process, a manual hearing detection mode is adopted to find out the fine performance of the equipment operation abnormality, however, the manual detection has the influence of artificial subjective factors, and the problem of low efficiency and effectiveness of equipment fault monitoring exists.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a substation equipment sound abnormality recognition method, apparatus, computer device, and storage medium capable of improving the efficiency and effectiveness of substation equipment fault monitoring.
A substation equipment voice anomaly identification method, the method comprising:
acquiring running sound data of monitored equipment in a transformer substation in real time;
extracting characteristics of the operation sound data to obtain a sound model to be distinguished for representing the operation state of the monitored equipment;
performing fault discrimination on the sound model to be discriminated and a normal sound model used for representing normal operation of the monitored equipment through a fault discrimination model, and outputting the offset of the sound model to be discriminated;
and determining the running state of the monitored equipment according to the offset.
In one embodiment, the acquiring, in real time, operation sound data of a monitored device in a substation includes:
acquiring operation sound data of monitored equipment in a transformer substation, which is acquired by acquisition equipment in real time; the acquisition device comprises a microphone and a sound data acquisition card used in a substation environment.
In one embodiment, the feature extraction of the operation sound data to obtain a sound model to be distinguished for characterizing the operation state of the monitored device includes:
extracting the characteristics of the operation sound data to obtain real-time characteristic parameters of the operation sound data;
and constructing a matrix set according to the real-time characteristic parameters to obtain a sound model to be distinguished, wherein the sound model is used for representing the running state of the monitored equipment.
In one embodiment, the determining the operation state of the monitored device according to the offset includes:
and when the offset is in a preset value interval, determining that the monitored equipment is in a normal running state.
In one embodiment, the method further comprises:
acquiring the operation parameters of the monitored equipment;
and when the operation parameters are preset operation parameters, updating the normal sound model into the sound model to be judged and updating the preset value interval, and executing the step of acquiring the operation sound data of the monitored equipment in the transformer substation.
In one embodiment, the determining the operation state of the monitored device according to the offset includes:
when the offset is not in the preset value interval, determining that the monitored equipment is in an abnormal operation state, and generating an alarm instruction; the alarm instruction is used for triggering alarm equipment on the monitored equipment to respond and generating alarm information.
In one embodiment, the method further comprises:
and sending the running state to a display terminal to perform visual display in different forms.
A substation equipment sound anomaly identification device, the device comprising:
the acquisition module is used for acquiring the operation sound data of the monitored equipment in the transformer substation in real time;
the feature extraction module is used for extracting features of the operation sound data to obtain a sound model to be distinguished, which is used for representing the operation state of the monitored equipment;
the fault judging module is used for carrying out fault judgment on the sound model to be judged and the normal sound model used for representing the normal operation of the monitored equipment through the fault judging model and outputting the offset of the sound model to be judged;
and the determining module is used for determining the running state of the monitored equipment according to the offset.
A computer device comprising a memory storing a computer program and a processor which when executing the computer program performs the steps of:
acquiring running sound data of monitored equipment in a transformer substation in real time;
extracting characteristics of the operation sound data to obtain a sound model to be distinguished for representing the operation state of the monitored equipment;
performing fault discrimination on the sound model to be discriminated and a normal sound model used for representing normal operation of the monitored equipment through a fault discrimination model, and outputting the offset of the sound model to be discriminated;
and determining the running state of the monitored equipment according to the offset.
A computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of:
acquiring running sound data of monitored equipment in a transformer substation in real time;
extracting characteristics of the operation sound data to obtain a sound model to be distinguished for representing the operation state of the monitored equipment;
performing fault discrimination on the sound model to be discriminated and a normal sound model used for representing normal operation of the monitored equipment through a fault discrimination model, and outputting the offset of the sound model to be discriminated;
and determining the running state of the monitored equipment according to the offset.
According to the method, the device, the computer equipment and the storage medium for identifying the voice abnormality of the transformer substation equipment, the voice model to be distinguished for representing the running state of the monitored equipment is obtained by extracting the running voice data of the monitored equipment in the transformer substation in real time; performing fault discrimination on the sound model to be discriminated and a normal sound model for representing normal operation of the monitored equipment through the fault discrimination model, and outputting the offset of the sound model to be discriminated; determining the running state of the monitored equipment according to the offset; the running sound data of the monitored equipment are collected in real time, the corresponding sound model to be judged is determined, the running state of the monitored equipment is obtained by comparing the offset between the sound model to be judged and the normal sound model, the influence of artificial subjective factors is eliminated, and the efficiency and the effectiveness of fault monitoring of the transformer substation equipment are improved.
Drawings
FIG. 1 is an application environment diagram of a substation equipment voice anomaly identification method in one embodiment;
fig. 2 is a flow chart of a method for identifying abnormal sound of a substation device in one embodiment;
FIG. 3 is a flowchart illustrating steps for identifying a voice abnormality of a substation device according to an embodiment;
fig. 4 is a flowchart of a method for identifying abnormal sound of a substation device according to another embodiment;
fig. 5 is a schematic diagram of an application scenario of voice anomaly recognition of substation equipment in one embodiment;
FIG. 6 is a block diagram of a device for identifying acoustic anomalies in substation equipment in one embodiment;
fig. 7 is an internal structural diagram of a computer device in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
The method for identifying the voice abnormality of the substation equipment can be applied to an application environment shown in fig. 1. Wherein the terminal 102 communicates with the server 104 via a network. The server 104 acquires operation sound data of monitored equipment in the transformer substation, which is acquired by the terminal 102 in real time; extracting characteristics of the operation sound data to obtain a sound model to be distinguished for representing the operation state of the monitored equipment; performing fault discrimination on the sound model to be discriminated and a normal sound model for representing normal operation of the monitored equipment through the fault discrimination model, and outputting the offset of the sound model to be discriminated; and determining the running state of the monitored equipment according to the offset. The terminal 102 may be, but is not limited to, an acquisition terminal for acquiring operation data of a substation device, and the acquisition terminal may be, but is not limited to, a personal computer, a notebook computer, a smart phone, a tablet computer, and a portable wearable device integrated with a substation-specific acquisition device (for example, a specific microphone), and the server 104 may be implemented by a stand-alone server or a server cluster formed by a plurality of servers.
In one embodiment, as shown in fig. 2, a method for identifying voice anomalies of substation equipment is provided, and the method is applied to the server in fig. 1 for illustration, and includes the following steps:
The operation sound data are acquired by the acquisition equipment on the acquisition terminal in response to the real-time acquisition instruction, the acquisition equipment comprises a microphone and a sound data acquisition card which are used in the transformer substation environment, and the special microphone is used for effectively shielding the influence of external environment noise, namely filtering the external environment noise to obtain the sound data of the monitored equipment after denoising during operation; the sound data acquisition card is used for acquiring data of the sound data of the denoised monitored equipment in operation, ensuring better frequency response, dynamic range and signal to noise ratio in the audible frequency range of sound, adapting to different industrial field environments and supporting data transmission modes of different network systems such as a wired network, a 4g network, a wifi network and the like.
Specifically, the operation sound data of at least one monitored device in the transformer substation is obtained by responding to a real-time acquisition instruction through an acquisition device on an acquisition terminal, the operation sound data acquired in real time is uploaded to a device sound database of a server for storage, and the acquired operation sound data is associated with a corresponding monitored device identifier.
And 204, extracting the characteristics of the operation sound data to obtain a sound model to be distinguished for representing the operation state of the monitored equipment.
The feature extraction is to perform voiceprint feature extraction on the collected operation sound data, namely Mel cepstrum coefficient feature extraction (Mel-scaleFrequency Cepstral Coefficients, MFCC); the MFCC feature extraction may be implemented by any conventional manner, which is not described herein.
Specifically, performing MFCC feature extraction on the received operation sound data to obtain real-time feature parameters of the operation sound data; and constructing a matrix set according to the real-time characteristic parameters to obtain the sound model to be distinguished for representing the running state of the monitored equipment. The sound model is used for representing the running state of specific equipment at a certain moment, such as normal running or abnormal running; the acoustic models of different devices are different.
And 206, performing fault discrimination on the sound model to be discriminated and the normal sound model for representing the normal operation of the monitored equipment through the fault discrimination model, and outputting the offset of the sound model to be discriminated.
The normal sound model is to pre-process (for example, pre-emphasis, framing, windowing and other modes) the operation sound data collected at the normal operation time according to the operation sound data collected at the normal operation time of the equipment in advance, and perform MFCC feature extraction on the pre-processed operation sound data to obtain feature parameters of the operation sound data in the normal state; and constructing a matrix set according to the characteristic parameters.
The fault judging model is trained in advance and is used for detecting the offset between the sound model to be judged and the normal sound model, namely, the characteristic values in the sound model to be judged at different moments of the monitored equipment are compared with the characteristic values of the normal sound model when the equipment operates normally, so that whether the sound model to be judged deviates from an established model greatly or not is judged, and whether the equipment operates normally or not is judged according to the offset degree.
Specifically, fault discrimination is performed on the to-be-discriminated sound models at different moments and the normal sound model of normal operation of the monitored equipment through pre-trained fault discrimination models, and offset of the to-be-discriminated sound models at different moments is output.
And step 208, determining the running state of the monitored equipment according to the offset.
The operation state of the monitored equipment comprises normal operation and abnormal operation.
Specifically, comparing the offset with a preset value interval, and determining that the monitored equipment is in a normal running state when the offset is in the preset value interval; when the offset is not in the preset value interval, determining that the monitored equipment is in an abnormal operation state; wherein the pre-set probability value is predetermined. For example, the offset is a value of 0-1, the preset value interval is 0.8-1, and when the offset is in the preset value interval and the value is closer to 1, the closer to the established model the sound to be discriminated is (i.e. the higher the similarity is); conversely, the closer to 0, the more the acoustic model to be discriminated deviates from the established model (i.e., the similarity is low).
In the method for identifying the abnormal sound of the transformer substation equipment, the sound model to be distinguished for representing the running state of the monitored equipment is obtained by extracting the running sound data of the monitored equipment in the transformer substation in real time; performing fault discrimination on the sound model to be discriminated and a normal sound model for representing normal operation of the monitored equipment through the fault discrimination model, and outputting the offset of the sound model to be discriminated; determining the running state of the monitored equipment according to the offset; the running sound data of the monitored equipment are collected in real time, the corresponding sound model to be judged is determined, the running state of the monitored equipment is obtained by comparing the offset between the sound model to be judged and the normal sound model, the influence of artificial subjective factors is eliminated, and the efficiency and the effectiveness of fault monitoring of the transformer substation equipment are improved.
In one embodiment, as shown in fig. 3, there is provided a substation equipment voice abnormality recognition step, which is described by taking an example that the method is applied to the server in fig. 1, and includes the following steps:
And step 304, extracting the characteristics of the operation sound data to obtain a sound model to be distinguished for representing the operation state of the monitored equipment.
And 306, performing fault discrimination on the sound model to be discriminated and a normal sound model for representing normal operation of the monitored equipment, and outputting the offset of the sound model to be discriminated.
At step 312, the operating parameters of the monitored device are obtained.
The operation parameters comprise operation time length of the substation equipment.
And step 314, when the operation parameter is the preset operation parameter, updating the normal sound model to the sound model to be judged and updating the preset value interval.
The transformer substation equipment generates loss along with the increase of the operation time, so that sound data of normal operation can be changed, namely, the characteristic extraction is carried out on the sound data of the normal operation, and the obtained characteristic parameters can be changed; that is, as the substation equipment operates, the characteristic parameters of the normal sound model are different at different periods.
Specifically, when the operation time length of the transformer substation equipment reaches the preset time length, acquiring operation sound data in a normal operation state at the current moment, performing feature extraction on the operation sound data to obtain current feature parameters of the operation sound data, constructing a matrix set according to the current feature parameters, taking the obtained current sound model as a normal sound model, updating, executing to acquire the operation sound data of monitored equipment in the transformer substation, and monitoring the operation state of the monitored equipment of the transformer substation.
Optionally, in one embodiment, the operation state of the monitored device is sent to the display terminal for visual display, so that the operation state of each device of the transformer substation can be obtained; when the monitored equipment is in an abnormal operation state, generating an alarm instruction; the alarm instruction is used for triggering alarm equipment on the monitored equipment to respond and generating alarm information; and sending the alarm information to a terminal where a maintenance person is located, timely informing the maintenance person of timely repairing the equipment fault.
In the step of identifying the abnormal sound of the transformer substation equipment, the sound model to be distinguished for representing the running state of the monitored equipment is obtained by extracting the running sound data of the monitored equipment in the transformer substation in real time; performing fault discrimination on the sound model to be discriminated and a normal sound model for representing normal operation of the monitored equipment through the fault discrimination model, and outputting the offset of the sound model to be discriminated; determining the running state of the monitored equipment by comparing the relation between the offset and a preset offset interval, and eliminating the influence of artificial subjective factors by comparing the offset between the sound model to be distinguished and the normal sound model, thereby improving the efficiency and the effectiveness of fault monitoring of the transformer substation equipment; in addition, the normal sound model of the monitored equipment is updated according to the operation parameters of the monitored equipment, and further, the accuracy of fault monitoring is improved.
In another embodiment, as shown in fig. 4, a method for identifying voice anomalies of substation equipment is provided, and the method is applied to the server in fig. 1 for illustration, and includes the following steps:
And step 404, extracting features of the operation sound data to obtain a sound model to be distinguished for representing the operation state of the monitored equipment.
And step 406, performing fault discrimination on the sound model to be discriminated and the normal sound model for representing the normal operation of the monitored equipment through the fault discrimination model, and outputting the offset of the sound model to be discriminated.
Alternatively, in one embodiment, the operating state is sent to the display terminal for visual display in a different form.
Specifically, the running state of the monitored equipment is sent to the display terminal, the running state is visually displayed in the form of a web page or displayed on an interface in the form of a hypertext language, and the real-time running state of the monitored equipment can be intuitively obtained.
And step 412, when the operation parameter is the preset operation parameter, updating the normal sound model to the sound model to be judged and updating the preset value interval.
Specifically, when the operation parameters are preset operation parameters, updating the normal sound model to the sound model to be judged and updating the preset value interval, and executing the step of acquiring the operation sound data of the monitored equipment in the transformer substation to acquire the real-time operation state of the monitored equipment.
And step 414, sending the running state to a display terminal to perform visual display in different forms.
Optionally, in one embodiment, when the offset is not within the preset value interval, determining that the monitored device is in an abnormal running state, and generating an alarm instruction; the alarm instruction is used for triggering alarm equipment on the monitored equipment to respond and generating alarm information; and sending the alarm information to a terminal where a maintenance person is located, timely informing the maintenance person of timely repairing the equipment fault.
The following is an application scenario of abnormal voice recognition of transformer substation equipment, as shown in fig. 5, operation voice data of monitored equipment in a transformer substation are acquired through a microphone on an acquisition terminal, the acquired operation voice data are transmitted to a server (which can be a cloud end or a local server) through data transmission modes of different network modes such as a wired network, a 4g network and a wifi network by an acquisition card (for example, a dual-channel acquisition card) connected with the microphone, the operation voice data are subjected to feature extraction in the server to obtain a to-be-discriminated voice model for representing the operation state of the monitored equipment, fault discrimination is performed on the to-be-discriminated voice model and a normal voice model for representing the normal operation of the monitored equipment by a fault discrimination model, the offset of the to-be-discriminated voice model is output, the operation state of the monitored equipment is determined according to the offset, and the obtained operation state is sent to a monitoring platform (for example, a client monitoring terminal) for visual display; the running sound data of the monitored equipment are collected in real time, the corresponding sound model to be judged is determined, the running state of the monitored equipment is determined by comparing the offset between the sound model to be judged and the normal sound model, the influence of artificial subjective factors is eliminated, and the efficiency and the effectiveness of fault monitoring of the transformer substation equipment are improved.
In the step of identifying the abnormal sound of the transformer substation equipment, the sound model to be distinguished for representing the running state of the monitored equipment is obtained by extracting the running sound data of the monitored equipment in the transformer substation in real time; performing fault discrimination on the sound model to be discriminated and a normal sound model for representing normal operation of the monitored equipment through the fault discrimination model, and outputting the offset of the sound model to be discriminated; the operation state of the monitored equipment is determined by comparing the relation between the offset and the preset offset interval, and when the monitored equipment is determined to be in the normal operation state, the normal sound model of the monitored equipment is updated according to the operation parameters of the monitored equipment, so that the accuracy of fault monitoring is further improved on the basis of improving the efficiency and the effectiveness of fault monitoring of the transformer substation equipment.
It should be understood that, although the steps in the flowcharts of fig. 2-4 are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in fig. 2-4 may include multiple steps or stages that are not necessarily performed at the same time, but may be performed at different times, nor does the order in which the steps or stages are performed necessarily performed in sequence, but may be performed alternately or alternately with at least a portion of the steps or stages in other steps or other steps.
In one embodiment, as shown in fig. 6, there is provided a substation equipment sound abnormality recognition apparatus, including: an acquisition module 602, a feature extraction module 604, a fault discrimination module 606, and a determination module 608, wherein:
the acquiring module 602 is configured to acquire, in real time, operation sound data of a monitored device in the substation.
The feature extraction module 604 is configured to perform feature extraction on the operation sound data to obtain a sound model to be distinguished, where the sound model is used for representing an operation state of the monitored device.
The fault discriminating module 606 is configured to perform fault discrimination on the to-be-discriminated acoustic model and the normal acoustic model for characterizing normal operation of the monitored device through the fault discriminating model, and output an offset of the to-be-discriminated acoustic model.
A determining module 608 is configured to determine an operation state of the monitored device according to the offset.
According to the voice abnormality recognition device for the transformer substation equipment, the voice model to be distinguished for representing the running state of the monitored equipment is obtained by extracting the running voice data of the monitored equipment in the transformer substation in real time; performing fault discrimination on the sound model to be discriminated and a normal sound model for representing normal operation of the monitored equipment through the fault discrimination model, and outputting the offset of the sound model to be discriminated; determining the running state of the monitored equipment according to the offset; the running sound data of the monitored equipment are collected in real time, the corresponding sound model to be judged is determined, the running state of the monitored equipment is obtained by comparing the offset between the sound model to be judged and the normal sound model, the influence of artificial subjective factors is eliminated, and the efficiency and the effectiveness of fault monitoring of the transformer substation equipment are improved.
In another embodiment, a substation equipment voice abnormality recognition apparatus is provided, which includes, in addition to the acquisition module 602, the feature extraction module 604, the fault discrimination module 606, and the determination module 608: the system comprises a construction module, an updating module and a visualization module, wherein:
in one embodiment, the obtaining module 602 is further configured to obtain operation sound data of a monitored device in the substation that is collected in real time by the collecting device; the acquisition device comprises a microphone and a sound data acquisition card used in the substation environment.
In one embodiment, the feature extraction module 604 is further configured to perform feature extraction on the running sound data, so as to obtain real-time feature parameters of the running sound data.
And the construction module is used for constructing a matrix set according to the real-time characteristic parameters to obtain the sound model to be distinguished for representing the running state of the monitored equipment.
In one embodiment, the determining module 608 is further configured to determine that the monitored device is in a normal operation state when the offset is within a preset value range.
In one embodiment, the acquisition module 602 is further configured to acquire an operating parameter of the monitored device.
And the updating module is used for updating the normal sound model into the sound model to be judged and updating the preset value interval when the operation parameter is the preset operation parameter.
In one embodiment, the determining module 608 is further configured to determine that the monitored device is in an abnormal operation state and generate an alarm instruction when the offset is not within the preset value interval; the alarm instruction is used for triggering alarm equipment on the monitored equipment to respond and generating alarm information.
And the visualization module is used for sending the running state to the display terminal to perform visualization display in different forms.
In one embodiment, the sound model to be distinguished for representing the running state of the monitored equipment is obtained by extracting the characteristics of running sound data of the monitored equipment in the transformer substation in real time; performing fault discrimination on the sound model to be discriminated and a normal sound model for representing normal operation of the monitored equipment through the fault discrimination model, and outputting the offset of the sound model to be discriminated; determining the running state of the monitored equipment by comparing the relation between the offset and the preset offset interval, and updating the normal sound model of the monitored equipment according to the running parameters of the monitored equipment when the monitored equipment is in the normal running state, so as to avoid false alarm caused by the change of the running time of the equipment; that is to say, the accuracy of fault monitoring is further improved on the basis of improving the efficiency and effectiveness of fault monitoring of the substation equipment; when the offset is not in the preset value interval, determining that the monitored equipment is in an abnormal operation state, and generating an alarm instruction; the alarm instruction is used for triggering alarm equipment on the monitored equipment to respond and generating alarm information; and the alarm information is sent to the terminal where the maintenance personnel are located, the maintenance personnel are informed in time, equipment faults are overhauled in time, and the safety of the transformer substation is improved.
The specific limitation of the substation equipment voice abnormality recognition device may be referred to the limitation of the substation equipment voice abnormality recognition method hereinabove, and will not be described herein. The above-mentioned each module in the substation equipment sound abnormality recognition device may be implemented in whole or in part by software, hardware, and a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, which may be a terminal, and the internal structure of which may be as shown in fig. 7. The computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless mode can be realized through WIFI, an operator network, NFC (near field communication) or other technologies. The computer program, when executed by a processor, implements a substation equipment sound abnormality identification method. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, can also be keys, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.
It will be appreciated by those skilled in the art that the structure shown in fig. 7 is merely a block diagram of some of the structures associated with the present application and is not limiting of the computer device to which the present application may be applied, and that a particular computer device may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided comprising a memory and a processor, the memory having stored therein a computer program, the processor when executing the computer program performing the steps of:
acquiring running sound data of monitored equipment in a transformer substation in real time;
extracting characteristics of the operation sound data to obtain a sound model to be distinguished for representing the operation state of the monitored equipment;
performing fault discrimination on the sound model to be discriminated and a normal sound model for representing normal operation of the monitored equipment through the fault discrimination model, and outputting the offset of the sound model to be discriminated;
and determining the running state of the monitored equipment according to the offset.
In one embodiment, the processor when executing the computer program further performs the steps of:
acquiring operation sound data of monitored equipment in a transformer substation, which is acquired by acquisition equipment in real time; the acquisition device comprises a microphone and a sound data acquisition card used in the substation environment.
In one embodiment, the processor when executing the computer program further performs the steps of:
extracting the characteristics of the operation sound data to obtain real-time characteristic parameters of the operation sound data;
and constructing a matrix set according to the real-time characteristic parameters to obtain the sound model to be distinguished for representing the running state of the monitored equipment.
In one embodiment, the processor when executing the computer program further performs the steps of:
and when the offset is in the preset value interval, determining that the monitored equipment is in the normal running state preset value interval. In one embodiment, the processor when executing the computer program further performs the steps of:
acquiring operation parameters of monitored equipment;
when the operation parameters are preset operation parameters, updating the normal sound model into the sound model to be judged and updating the preset value interval, and executing the step of acquiring the operation sound data of the monitored equipment in the transformer substation.
In one embodiment, the processor when executing the computer program further performs the steps of:
when the offset is not in the preset value interval, determining that the monitored equipment is in an abnormal operation state, and generating an alarm instruction; the alarm instruction is used for triggering alarm equipment on the monitored equipment to respond and generating alarm information.
In one embodiment, the processor when executing the computer program further performs the steps of:
and sending the running state to a display terminal to perform visual display in different forms.
In one embodiment, a computer readable storage medium is provided having a computer program stored thereon, which when executed by a processor, performs the steps of:
acquiring running sound data of monitored equipment in a transformer substation in real time;
extracting characteristics of the operation sound data to obtain a sound model to be distinguished for representing the operation state of the monitored equipment;
performing fault discrimination on the sound model to be discriminated and a normal sound model for representing normal operation of the monitored equipment through the fault discrimination model, and outputting the offset of the sound model to be discriminated;
and determining the running state of the monitored equipment according to the offset.
In one embodiment, the computer program when executed by the processor further performs the steps of:
acquiring operation sound data of monitored equipment in a transformer substation, which is acquired by acquisition equipment in real time; the acquisition device comprises a microphone and a sound data acquisition card used in the substation environment.
In one embodiment, the computer program when executed by the processor further performs the steps of:
extracting the characteristics of the operation sound data to obtain real-time characteristic parameters of the operation sound data;
and constructing a matrix set according to the real-time characteristic parameters to obtain the sound model to be distinguished for representing the running state of the monitored equipment.
In one embodiment, the computer program when executed by the processor further performs the steps of:
and when the offset is in a preset value interval, determining that the monitored equipment is in a normal running state. In one embodiment, the computer program when executed by the processor further performs the steps of:
acquiring operation parameters of monitored equipment;
when the operation parameters are preset operation parameters, updating the normal sound model into the sound model to be judged and updating the preset value interval, and executing the step of acquiring the operation sound data of the monitored equipment in the transformer substation.
In one embodiment, the computer program when executed by the processor further performs the steps of:
when the offset is not in the preset value interval, determining that the monitored equipment is in an abnormal operation state, and generating an alarm instruction; the alarm instruction is used for triggering alarm equipment on the monitored equipment to respond and generating alarm information.
In one embodiment, the computer program when executed by the processor further performs the steps of:
and sending the running state to a display terminal to perform visual display in different forms.
Those skilled in the art will appreciate that implementing all or part of the above-described methods in accordance with the embodiments may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, or the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), and the like.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples merely represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the invention. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application is to be determined by the claims appended hereto.
Claims (10)
1. A method for identifying a sound abnormality of a substation device, the method comprising:
acquiring operation sound data of monitored equipment in a transformer substation, which is acquired by acquisition equipment in real time; the acquisition equipment comprises a microphone and a sound data acquisition card which are used in a transformer substation environment;
acquiring the operation parameters of the monitored equipment; the operation parameters comprise the operation time length of the monitored equipment;
extracting characteristics of the operation sound data to obtain a sound model to be distinguished for representing the operation state of the monitored equipment;
performing fault discrimination on the sound model to be discriminated and a normal sound model used for representing normal operation of the monitored equipment through a fault discrimination model, and outputting the offset of the sound model to be discriminated;
determining the running state of the monitored equipment according to the offset;
the determining the running state of the monitored equipment according to the offset comprises the following steps:
when the offset is in a preset value interval, determining that the monitored equipment is in a normal running state preset value interval;
and when the operation parameters are preset operation parameters, updating the normal sound model into the sound model to be judged and updating the preset value interval, and executing the step of acquiring the operation sound data of the monitored equipment in the transformer substation, which is acquired by the acquisition equipment in real time.
2. The method according to claim 1, wherein the feature extracting the operation sound data to obtain a sound model to be discriminated for characterizing the operation state of the monitored device includes:
extracting the characteristics of the operation sound data to obtain real-time characteristic parameters of the operation sound data;
and constructing a matrix set according to the real-time characteristic parameters to obtain a sound model to be distinguished, wherein the sound model is used for representing the running state of the monitored equipment.
3. The method of claim 2, wherein the feature extracting the operation sound data comprises:
and extracting the mel-frequency spectrum coefficient characteristics of the operation sound data.
4. The method of claim 1, wherein said determining the operational status of the monitored device based on the offset comprises:
when the offset is not in the preset value interval, determining that the monitored equipment is in an abnormal operation state, and generating an alarm instruction; the alarm instruction is used for triggering alarm equipment on the monitored equipment to respond and generating alarm information.
5. The method according to claim 1, wherein the method further comprises:
and sending the running state to a display terminal to perform visual display in different forms.
6. The method of claim 1, wherein the manner in which the normal acoustic model is derived comprises:
and collecting the operation sound data of the equipment in the normal operation time, preprocessing the operation sound data in the normal operation time and extracting the characteristic of the mel-frequency spectrum coefficient to obtain the characteristic parameters of the operation sound data in the normal operation state, and constructing a matrix for the characteristic parameters of the operation sound data in the normal operation state to obtain the normal sound model.
7. The method of claim 1, wherein the operational status of the monitored device includes normal operation and abnormal operation.
8. A substation equipment sound abnormality identification device, characterized in that the device comprises:
the acquisition module is used for acquiring the operation sound data of the monitored equipment in the transformer substation, which is acquired by the acquisition equipment in real time; the acquisition equipment comprises a microphone and a sound data acquisition card which are used in a transformer substation environment;
the acquisition module is also used for acquiring the operation parameters of the monitored equipment; the operation parameters comprise the operation time length of the monitored equipment;
the feature extraction module is used for extracting features of the operation sound data to obtain a sound model to be distinguished, which is used for representing the operation state of the monitored equipment;
the fault judging module is used for carrying out fault judgment on the sound model to be judged and the normal sound model used for representing the normal operation of the monitored equipment through the fault judging model and outputting the offset of the sound model to be judged;
the determining module is used for determining the running state of the monitored equipment according to the offset;
the determining module is further used for determining that the monitored equipment is in a preset value interval in a normal running state when the offset is in the preset value interval;
and the updating module is used for updating the normal sound model into the sound model to be judged and updating the preset value interval when the operation parameter is a preset operation parameter.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 7 when the computer program is executed.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110686609.9A CN113670434B (en) | 2021-06-21 | 2021-06-21 | Method and device for identifying sound abnormality of substation equipment and computer equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110686609.9A CN113670434B (en) | 2021-06-21 | 2021-06-21 | Method and device for identifying sound abnormality of substation equipment and computer equipment |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113670434A CN113670434A (en) | 2021-11-19 |
| CN113670434B true CN113670434B (en) | 2023-05-02 |
Family
ID=78538265
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110686609.9A Active CN113670434B (en) | 2021-06-21 | 2021-06-21 | Method and device for identifying sound abnormality of substation equipment and computer equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113670434B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114964467B (en) * | 2022-03-21 | 2023-01-13 | 珠海格力电器股份有限公司 | Equipment detection method and device, sound collection equipment and storage medium |
| CN116793653B (en) * | 2023-06-21 | 2024-02-27 | 北京谛声科技有限责任公司 | Acoustic signal-based full life cycle monitoring method and system for rotating equipment |
| CN117409815B (en) * | 2023-12-13 | 2024-06-25 | 深圳海辰储能科技有限公司 | PCS fault detection method and related device for energy storage converter |
Family Cites Families (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2006189334A (en) * | 2005-01-06 | 2006-07-20 | Chugoku Electric Power Co Inc:The | Facility inspecting system |
| JP2009257862A (en) * | 2008-04-15 | 2009-11-05 | Original Engineering Consultants Co Ltd | Method for diagnosing integrity in equipment such as rotary machine by sound signal |
| US9964625B2 (en) * | 2011-06-27 | 2018-05-08 | General Electric Company | Electrical substation fault monitoring and diagnostics |
| JP2013200143A (en) * | 2012-03-23 | 2013-10-03 | Mitsubishi Electric Corp | Abnormal sound diagnosis device and abnormal sound diagnosis system |
| CN103971702A (en) * | 2013-08-01 | 2014-08-06 | 哈尔滨理工大学 | Sound monitoring method, device and system |
| JP2016090461A (en) * | 2014-11-07 | 2016-05-23 | 公益財団法人鉄道総合技術研究所 | Construction method for abnormality diagnostic functional model for sound of monitored object, and construction system therefor |
| US20160343180A1 (en) * | 2015-05-19 | 2016-11-24 | GM Global Technology Operations LLC | Automobiles, diagnostic systems, and methods for generating diagnostic data for automobiles |
| CN106910495A (en) * | 2017-04-26 | 2017-06-30 | 中国科学院微电子研究所 | Audio classification system and method applied to abnormal sound detection |
| ES2970507T3 (en) * | 2017-09-06 | 2024-05-29 | Nippon Telegraph & Telephone | Abnormality model learning device, method and program |
| CN107860469B (en) * | 2017-11-22 | 2019-09-10 | 重庆大学 | A kind of substation's noise prediction method based on way of fitting |
| JP6930503B2 (en) * | 2018-07-20 | 2021-09-01 | 日本電信電話株式会社 | Anomaly detection device, anomaly detection method, and program |
| CN109357749B (en) * | 2018-09-04 | 2020-12-04 | 南京理工大学 | DNN algorithm-based power equipment audio signal analysis method |
| CN111354366B (en) * | 2018-12-20 | 2023-06-16 | 沈阳新松机器人自动化股份有限公司 | Abnormal sound detection method and abnormal sound detection device |
| CN109754581A (en) * | 2019-01-15 | 2019-05-14 | 深圳市宇航智造技术有限公司 | Calculator room equipment method for early warning, device, equipment and storage medium |
| JP7133512B2 (en) * | 2019-06-11 | 2022-09-08 | 株式会社日立製作所 | Automatic inspection system |
| CN112289341A (en) * | 2020-11-03 | 2021-01-29 | 国网智能科技股份有限公司 | Sound abnormity identification method and system for transformer substation equipment |
-
2021
- 2021-06-21 CN CN202110686609.9A patent/CN113670434B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN113670434A (en) | 2021-11-19 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113670434B (en) | Method and device for identifying sound abnormality of substation equipment and computer equipment | |
| EP3759558B1 (en) | Intelligent audio analytic apparatus (iaaa) and method for space system | |
| EP3759789B1 (en) | System and method for audio and vibration based power distribution equipment condition monitoring | |
| CN111708687B (en) | Equipment abnormality index determination method, device, equipment and storage medium | |
| CN114548437A (en) | Intelligent operation and maintenance system and method for rotating equipment based on industrial Internet of things | |
| CN115145788A (en) | Detection data generation method and device for intelligent operation and maintenance system | |
| CN114255784A (en) | Substation equipment fault diagnosis method based on voiceprint recognition and related device | |
| CN112396250A (en) | Diesel engine fault prediction method, device, equipment and storage medium | |
| CN115840676A (en) | Computer hardware fault early warning method and system based on multidimensional data | |
| CN117937768B (en) | Intelligent electrical cabinet remote monitoring system | |
| CN117217730A (en) | Power equipment fault identification method, device, equipment, medium and product | |
| CN118051822A (en) | Equipment running state abnormality detection method based on voiceprint recognition technology | |
| CN117975999A (en) | Valve cooling equipment fault detection method and device, computer equipment and storage medium | |
| CN112256470B (en) | Fault server positioning method and device, storage medium and electronic equipment | |
| CN117994955A (en) | Method and device for building and alarming temperature alarm model of hydroelectric generating set | |
| CN114252906A (en) | Sound event detection method and device, computer equipment and storage medium | |
| CN110365642A (en) | Method, apparatus, computer equipment and the storage medium of monitoring information operation | |
| CN116520143B (en) | Voiceprint data-based rotating equipment monitoring method, device, equipment and medium | |
| CN114439405B (en) | Method, device, equipment and medium for monitoring faults of blowout preventer control device | |
| RU2793797C2 (en) | Intelligent audio-analytical device and method for spacecrafts | |
| CN118470649B (en) | Distribution network cable environment sensing method and system based on double-layer sensing | |
| CN117636909B (en) | Data processing method, device, equipment and computer readable storage medium | |
| CN117640636A (en) | Cloud computing-based dynamic ring monitoring method and system | |
| CN118070104A (en) | Fire-fighting equipment fault arranging and detecting method and system based on artificial intelligence | |
| CN116682454A (en) | Intelligent audio-based railway equipment acoustic monitoring system and method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |