CN110146764A - A kind of power equipment method for detecting abnormality based on acoustic array edge calculations - Google Patents
A kind of power equipment method for detecting abnormality based on acoustic array edge calculations Download PDFInfo
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- CN110146764A CN110146764A CN201910516444.3A CN201910516444A CN110146764A CN 110146764 A CN110146764 A CN 110146764A CN 201910516444 A CN201910516444 A CN 201910516444A CN 110146764 A CN110146764 A CN 110146764A
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- 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
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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
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Abstract
The invention patent provides one kind and is able to solve power transmission and transforming equipment in charging operation, carries out acoustic information collection to power transmission and transforming equipment and carries out the power equipment method for detecting abnormality based on acoustic array edge calculations of accident analysis, intelligent early-warning to equipment state；The power equipment method for detecting abnormality based on acoustic array edge calculations uses power equipment abnormal detector, and the power equipment abnormal detector includes acoustic matrix sensor, data reception module, control module, memory module, edge calculations module, communication module and Cloud Server platform；It is further comprising the steps of: 1) the first data of power equipment operating index to be acquired；2) it is then converted by data；3) then data are handled and is distributed；4) processing to data is then realized by each functional unit；It can be realized intelligent trouble using the power equipment method for detecting abnormality based on acoustic array edge calculations to differentiate, realize intelligent early-warning.
Description
Technical field
The invention patent relates to a kind of transmission facility operating status method for detecting abnormality based on acoustic array edge calculations, tool
Body is related to a kind of power equipment method for detecting abnormality based on acoustic array edge calculations.
Background technique
The growth of power grid scale and the increase of transformer equipment quantity, so that the distributed areas of power grid constantly expand；Meanwhile with
Electric system reform deepen continuously, electric power enterprise improve service quality, guarantee customer power supply reliability while, drop
The operation cost of low enterprise also becomes one of the main target of its pursuit.With the increase at full speed of power transmission and transforming equipment quantity, cause
The increase of equipment self-operating failure, especially large-scale power transmission and transforming equipment longtime running；Once power transmission and transforming equipment breaks down, meeting
It causes equipment itself damage even large-area power-cuts occur, causes huge economic loss and social influence.
It is also at present that a difficult point since structure is complicated for power transmission and transforming equipment leads to equipment to power transmission and transforming equipment malfunction elimination
The factor of operation troubles is numerous, and the feature that equipment breaks down also tends to be multifarious, even if same equipment is when different
Between, the same fault that occurs of place, fault signature is also not necessarily identical, between the failure symptom and failure of equipment often
There is no one-to-one mapping relations, therefore carry out fault diagnosis to power transmission and transforming equipment, if only by equipment operation rule
With the functional relation between the equipment operating parameter that has been determined in advance, be difficult to carry out equipment effectively, accurately fault diagnosis.It is existing
Detection device only have data recording function without having real-time analytic function.And each power transmission and transforming equipment is independent
Individual cannot achieve the data communication between equipment, need by Cloud Server to be support.But traditional Cloud Server can not
Meet the demands such as low latency, high reliability and data safety.
Patent content
Problems solved by the invention is to provide one kind and is able to solve power transmission and transforming equipment in charging operation, to power transmission and transformation
Equipment carries out acoustic information collection and carries out accident analysis, intelligent early-warning to equipment state, to solve to mention in above-mentioned background technique
The power equipment method for detecting abnormality based on acoustic array edge calculations out.
In order to achieve the above object, technical solution used by the invention patent is: one kind being based on acoustic array edge calculations
Power equipment method for detecting abnormality, using power equipment abnormal detector, the power equipment abnormal detector includes
Acoustic matrix sensor, data reception module, control module, memory module, edge calculations module, communication module and Cloud Server
Platform；
The acoustic matrix sensor, data reception module and control module are once electrically connected；The memory module, edge
Computing module and communication module are electrically connected with control module respectively；
The acoustic matrix sensor is used to acquire the first data of power equipment operating index；
It is made of multiple MEMS sonic transducers, using the sound transducer device of the equidistant permutation and combination of multiple lines and multiple rows；
The data reception module includes charge amplifier, filter circuit, normalization amplifier, D/A converting circuit；
The charge amplifier receives the analog signal of acoustic matrix sensor output, converts analog signals into voltage signal
After be output to filter circuit；
The filter circuit carries out analog signal to put straight and filtering, is output to kiichi amplifier；The normalization is put
Big device will filter after later analog signal is normalized by the requirement of measurement range, and the analog signal of output passes through
D/A converting circuit is exported to control module；
The control module is used to carry out the scheduling of resource and data distribution of whole system, receives and comes from data reception module
Original signal, the original signal received carried out by control module by memory module, communication module, edge calculations module
Unified distribution management；
The memory module is for receiving the instruction from control module, for storing the original signal received and side
Edge algorithm model；
The communication module is used to receive the instruction for carrying out cloud service platform and is output to control module progress data parsing, simultaneously
Data of the reception from control module are exported to Cloud Server platform progress data and are reported；
The edge calculations module includes 8 core cpus, it may be assumed that the first CPU, the 4th CPU the 5th of the 2nd the 3rd CPU of CPU
The 8th CPU and HWCE hardware convolutional engine of the 6th the 7th CPU of CPU of CPU, the edge calculations module is for receiving from control mould
The initial data of block output, realizes edge calculations, extracts the relevant informations such as the characteristic value of initial data and carries out to failure sound source
Precise positioning；
It is further comprising the steps of:
1) the first data of power equipment operating index are acquired by acoustic matrix sensor first；
2) analog signal that the output of acoustic matrix sensor is then received by data reception module, converts analog signals into
After voltage signal, then put straight and filtering to analog signal, by filter later analog signal by measurement range requirement into
After row normalized, the analog signal of output is exported by D/A converting circuit to control module；
3) scheduling of resource of whole system is then carried out by control module and data is distributed, received and come from data reception
The original signal of block, the original signal received pass through memory module, communication module, edge calculations module；
4) then by memory module reception the instruction from control module, for store the original signal received and
Edge algorithms model；
Control module is output to come the instruction of cloud service platform by communication module reception and carries out data parsing, is received simultaneously
Data from control module are exported to Cloud Server platform progress data and are reported；
The initial data exported from control module is received by edge calculations module, realizes edge calculations, is extracted original
The relevant informations such as the characteristic value of data simultaneously carry out precise positioning to failure sound source.
Further, the data that the communication module is sent to Cloud Server platform in step 4 are the number of power equipment
According to the data of the power equipment include the combination of following one or more:
Send the data to the Cloud Server so that Cloud Server storage power equipment sound record data；
Send the data to the Cloud Server so that Cloud Server storage power equipment sound characteristic value data；
Send the data to the Cloud Server so that Cloud Server storage power equipment sound malfunction with
Warning data.
Further, the control module from the memory module imports CNN intelligent algorithm model, institute in step 4
It states audio data signal and data and the execution CNN calculation of HWCE hardware convolutional engine is divided by parallel C PU in edge calculations module
Method model extracts the characteristic value information, fault message.
Further, the module of the edge calculations include 8 parallel C PU cores and a HWCE core, therein 8
A CPU Parallel Design can carry out Fragmentation to initial data, realize parallel processing；HWCE core is the CNN of a low-power consumption
Model treatment engine, integrating parallel processing CPU realize that initial data quickly calculates and obtains corresponding characteristic value and relevant information.
Further, the acoustic matrix sensor is formed based on linear voice sensor, by 16 sound transducers according to 4
The column of row 4 are spaced substantially equidistant, and carry out acoustic matrix sensor multi-channel data acquisition.
Beneficial effects of the present invention: the power equipment method for detecting abnormality of the present invention based on acoustic array edge calculations
Application is calculated using acoustic matrix sensor jointing edge to detect for the diagnosis of status of electric power, is sensed by multi-channel sound
The controllable waveform of acoustic array for the acoustic matrix sensor group that device is combined into detects the first data of the sound of power equipment, realization pair
Failure sound source precise positioning；
The audio data signal acquired by acoustic matrix sensor, by edge calculations module, to the sound of power equipment
Data extract correlated characteristic state of value and carry out positioning of beam and intelligent trouble differentiation.
When the first data for determining the power equipment index are collected by acoustic array, by the power equipment index
First data and calculated characteristic value information, fault message are sent to Cloud Server platform.
Therefore, the power equipment method for detecting abnormality of the present invention based on acoustic array edge calculations is able to solve defeated change
Electric equipment in charging operation, to power transmission and transforming equipment carry out acoustic information collection and to equipment state carry out accident analysis,
Intelligent early-warning.
Detailed description of the invention
Fig. 1 is the functional block diagram of power equipment abnormal detector in the embodiment of the present invention；
Fig. 2 is the flow chart of data reception module in inventive embodiments；
Fig. 3 is the functional diagram of edge calculations module in inventive embodiments；
Wherein, 1, acoustic matrix sensor；2, data reception module；3, control module；4, memory module；5, communication module；
6, edge calculations module；7, Cloud Server platform；8, charge amplifier；9, filter circuit；10, amplifier is normalized；11, number
Analog conversion circuit；20, hardware convolutional engine.
Specific embodiment
The specific embodiment of the invention patent is described in detail with reference to the accompanying drawing.
In the embodiment of the invention patent, as shown in Fig. 1 to 2, a kind of power equipment based on acoustic array edge calculations is different
Normal detection method, using power equipment abnormal detector, the power equipment abnormal detector includes acoustic matrix sensor
1, data reception module 2, control module 3, memory module 4, edge calculations module 6, communication module 5 and Cloud Server platform 7；
The acoustic matrix sensor 1, data reception module 2 and control module 3 are once electrically connected；The memory module 4,
Edge calculations module 6 and communication module 5 are electrically connected with control module 3 respectively；
The acoustic matrix sensor 1 is used to acquire the first data of power equipment operating index；
It is made of multiple MEMS sonic transducers, using the sound transducer device of the equidistant permutation and combination of multiple lines and multiple rows；
The data reception module 2 includes charge amplifier 8, filter circuit 9, normalization amplifier 10, digital-to-analogue conversion electricity
Road 11；
The charge amplifier 8 receives the analog signal that acoustic matrix sensor 1 exports, and converts analog signals into voltage letter
Filter circuit 9 is output to after number；
The filter circuit 9 carries out analog signal to put straight and filtering, is output to kiichi amplifier 10；The normalization
Amplifier 10 will filter after later analog signal is normalized by the requirement of measurement range, the analog signal of output
It is exported by D/A converting circuit to control module 3；
The control module 3 is used to carry out the scheduling of resource and data distribution of whole system, receives and comes from data reception
The original signal of block 2, the original signal received is by memory module 4, communication module 5, edge calculations module 6, by control mould
Block 3 carries out unified distribution management；
The memory module 4 for receiving the instruction from control module 3, for store the original signal received and
Edge algorithms model；
The communication module 5 is used to receive the instruction for carrying out cloud service platform 7 and is output to the progress data parsing of control module 3,
Data of the reception from control module 3 are exported to the progress of Cloud Server platform 7 data and are reported simultaneously；
The edge calculations module 6 includes 8 core cpus, it may be assumed that the first CPU12, the 3rd CPU14 the 4th of the 2nd CPU13
The 8th CPU19 and HWCE hardware convolutional engine 20 of the 5th the 6th the 7th CPU18 of CPU17 of CPU16 of CPU15, the edge calculations mould
Block 6 is used to receive the initial data from control module output, realizes edge calculations, extracts the correlations such as the characteristic value of initial data
Information simultaneously carries out precise positioning to failure sound source；
It is further comprising the steps of:
1) the first data of power equipment operating index are acquired by acoustic matrix sensor 1 first；
2) analog signal that acoustic matrix sensor 1 exports then is received by data reception module 2, analog signal is converted
After voltage signal, then analog signal is carried out to put straight and filtering, later analog signal will be filtered by the requirement of measurement range
After being normalized, the analog signal of output is exported by D/A converting circuit to control module 3；
3) scheduling of resource and data distribution that whole system is then carried out by control module 3, receive and come from data receiver
The original signal of module 2, the original signal received pass through memory module 4, communication module 5, edge calculations module 6；
4) then by memory module 4 receive the instruction from control module 3, for store the original signal received with
And edge algorithms model；
It is received by communication module 5 and is output to the progress data parsing of control module 3 come the instruction of cloud service platform 7, simultaneously
Data of the reception from control module 3 are exported to the progress of Cloud Server platform 7 data and are reported；
The initial data exported from control module is received by edge calculations module 6, realizes edge calculations, is extracted original
The relevant informations such as the characteristic value of data simultaneously carry out precise positioning to failure sound source.
Specifically, the power equipment abnormal detector includes acoustic matrix sensor 1, data reception module 2 controls mould
Block 3, memory module 4, edge calculations module 5, communication module 6.
Acoustic matrix sensor 1 is used to acquire the first data of power equipment operating index, by multiple MEMS sonic transducer groups
At using the sound transducer device of the equidistant permutation and combination of multiple lines and multiple rows；
Data reception module 2 by charge amplifier 8, filter circuit 9, normalization amplifier 10,11 groups of D/A converting circuit
At the analog signal of 8 receiving sensor of the charge amplifier output is output to filter after converting analog signals into voltage signal
Wave circuit 9, the filter circuit 9 carry out analog signal to put straight and filtering, are output to kiichi amplifier 10, the normalization
Amplifier 10 will filter after later analog signal is normalized by the requirement of measurement range, the analog signal of output
It is exported by D/A converting circuit to control module 3；
The control module 3 is used to carry out the scheduling of resource and data distribution of whole system, receives and comes from data reception
The original signal of block 2, the original signal received is by memory module 4, communication module 5, edge calculations module 6, by control mould
Block 3 carries out unified distribution management；Different by the operating mode of control module, the digital signal of input control module 3 is by controlling
Module 3 is stored in memory module, can also be exported by control module 3 to edge calculations module 6 and be carried out data processing, in conjunction with CNN
The characteristic value and relevant information of intelligent algorithm extraction digital signal；
The memory module 4 for receiving the instruction from control module 3, for store the original signal received and
Edge algorithms model
The communication module 5 is used to receive the instruction for carrying out cloud service platform 7 and is output to the progress data parsing of control module 3,
Data of the reception from control module 3 are exported to the progress of Cloud Server platform 7 data and are reported simultaneously；
The edge calculations module 6 is used to receive the initial data from control module output, passes through 8 core cpus 12
20 composition of~19 and HWCE hardware convolutional engine composition, realizes edge calculations, extracts the related letter such as characteristic value of initial data
Breath；
Specifically, the data that the communication module 5 is sent to Cloud Server platform 7 in step 4 are the number of power equipment
According to the data of the power equipment include the combination of following one or more:
Send the data to the Cloud Server so that Cloud Server storage power equipment sound record data；
Send the data to the Cloud Server so that Cloud Server storage power equipment sound characteristic value data；
Send the data to the Cloud Server so that Cloud Server storage power equipment sound malfunction with
Warning data.
Specifically, the control module 3 from the memory module 4 imports CNN intelligent algorithm model, institute in step 4
It states audio data signal and data and HWCE hardware convolutional engine execution CNN is divided by parallel C PU in edge calculations module 6
Algorithm model extracts the characteristic value information, fault message.
The module 6 of the edge calculations includes 8 parallel C PU cores and a HWCE core, and 8 CPU therein are parallel
Design can carry out Fragmentation to initial data, realize parallel processing；HWCE core is that the CNN model treatment an of low-power consumption draws
It holds up, integrating parallel processing CPU realizes that initial data quickly calculates and obtains corresponding characteristic value and relevant information.
The acoustic matrix sensor 1 is formed based on linear voice sensor, is arranged by 16 sound transducers according to 4 rows 4
Distance arrangement, carries out acoustic matrix sensor multi-channel data acquisition.
As shown in figure 3, control module 3 exports the digital signal that data reception module 2 exports to edge calculations module 6,
Edge calculations module 6 carries out piecemeal according to data signal content, distributes to the progress data prediction of parallel C PU 12~19 and obtains
Then preprocessed data is exported preprocessed data to HWCE hardware convolutional engine 20, HWCE hardware convolutional engine 20 is according to pre-
The CNN model of definition, preprocessed data pass through CNN intelligent algorithm model after calculate acoustic matrix column signal characteristic value and
Its related data, and characteristic value data is returned into control module 3, control module 3 passes through storage mould according to by characteristic value data
Block 4 store and characteristic value data is uploaded to Cloud Server platform 7 by communication module 5.
As shown in Figure 1, communication module 5 is responsible for receiving the instruction for carrying out cloud service platform 7 and CNN algorithm model is output to
Control module 3 carries out data parsing and is stored by memory module 4, while receiving the data from control module 3 and exporting to cloud
Server platform 7 carries out data and reports.
Although being described in detail in conjunction with specific embodiment of the attached drawing to the invention patent, should not be construed as pair
The restriction of the protection scope of this patent.In range described by claims, those skilled in the art are without creative labor
The dynamic various modifications that can be made and deformation still fall within the protection scope of this patent.
Claims (5)
1. a kind of power equipment method for detecting abnormality based on acoustic array edge calculations, it is characterised in that: different using power equipment
Normal detection device, the power equipment abnormal detector include acoustic matrix sensor (1), data reception module (2), control mould
Block (3), memory module (4), edge calculations module (6), communication module (5) and Cloud Server platform (7)；
The acoustic matrix sensor (1), data reception module (2) and control module (3) are once electrically connected；The memory module
(4), edge calculations module (6) and communication module (5) are electrically connected with control module (3) respectively；
The acoustic matrix sensor (1) is used to acquire the first data of power equipment operating index；
It is made of multiple MEMS sonic transducers, using the sound transducer device of the equidistant permutation and combination of multiple lines and multiple rows；
The data reception module (2) includes charge amplifier (8), filter circuit (9), normalization amplifier (10), and digital-to-analogue turns
Change circuit (11)；
The charge amplifier (8) receives the analog signal of acoustic matrix sensor (1) output, converts analog signals into voltage letter
Filter circuit (9) are output to after number；
The filter circuit (9) carries out analog signal to put straight and filtering, is output to kiichi amplifier (10)；The normalization
Amplifier (10) will filter after later analog signal is normalized by the requirement of measurement range, the simulation letter of output
It number is exported by D/A converting circuit to control module (3)；
The control module (3) is used to carry out the scheduling of resource and data distribution of whole system, receives and comes from data reception module
(2) original signal, the original signal received pass through memory module (4), communication module (5), edge calculations module (6), by
Control module (3) carries out unified distribution management；
The memory module (4) be used for receives come from control module (3) instruction, for store the original signal received and
Edge algorithms model；
The communication module (5) is used to receive the instruction for carrying out cloud service platform (7) and is output to control module (3) progress data solution
Analysis, while reception is exported to Cloud Server platform (7) progress data from the data of control module (3) and is reported；
The edge calculations module (6) includes 8 core cpus, it may be assumed that the first CPU (12), the 3rd CPU (14) of the 2nd CPU (13) the
The 8th CPU (19) of four the 5th the 6th the 7th CPU (18) of CPU (17) of CPU (16) of CPU (15) and HWCE hardware convolutional engine (20),
The edge calculations module (6) is used to receive the initial data from control module output, realizes edge calculations, extracts original number
According to the relevant informations such as characteristic value and precise positioning is carried out to failure sound source；
It is further comprising the steps of:
1) the first data of power equipment operating index are acquired by acoustic matrix sensor (1) first；
2) analog signal that acoustic matrix sensor (1) output is then received by data reception module (2), analog signal is converted
After voltage signal, then analog signal is carried out to put straight and filtering, later analog signal will be filtered by the requirement of measurement range
After being normalized, the analog signal of output is exported by D/A converting circuit to control module (3)；
3) scheduling of resource and data distribution that whole system is then carried out by control module (3), receive and come from data reception
The original signal of block (2), the original signal received pass through memory module (4), communication module (5), edge calculations module (6)；
4) then by memory module (4) receive come from control module (3) instruction, for store the original signal received with
And edge algorithms model；
It is received by communication module (5) and is output to control module (3) progress data parsing come the instruction of cloud service platform (7), together
When receive from control module (3) data export to Cloud Server platform (7) carry out data report；
The initial data exported from control module is received by edge calculations module (6), realizes edge calculations, extracts original number
According to the relevant informations such as characteristic value and precise positioning is carried out to failure sound source.
2. the method for detecting abnormality according to claim 1 based on acoustic array edge calculations, it is characterised in that: in step 4)
The data that the communication module (5) sends to Cloud Server platform (7) are the data of power equipment, the data of the power equipment
Combination including following one or more:
Send the data to the Cloud Server so that Cloud Server storage power equipment sound record data；
Send the data to the Cloud Server so that Cloud Server storage power equipment sound characteristic value data；
Send the data to the Cloud Server so that Cloud Server storage power equipment sound malfunction and early warning
Data.
3. edge calculations module according to claim 1, it is characterised in that: the control module described in step 4) (3) from
The memory module (4) imports CNN intelligent algorithm model, and the audio data signal passes through in edge calculations module (6)
Parallel C PU divides data and HWCE hardware convolutional engine executes CNN algorithm model, extracts the characteristic value information, failure
Information.
4. edge calculations module according to claim 1, it is characterised in that: the module (6) of the edge calculations includes 8
Parallel C PU core and a HWCE core, 8 CPU Parallel Designs therein can carry out Fragmentation to initial data, realize simultaneously
Row processing；HWCE core is the CNN model treatment engine an of low-power consumption, and integrating parallel handles CPU and realizes that initial data is quick
It calculates and obtains corresponding characteristic value and relevant information.
5. edge calculations module according to claim 1, it is characterised in that: the acoustic matrix sensor (1) is based on linear
Sound transducer composition is spaced substantially equidistant according to 4 rows 4 column by 16 sound transducers, carries out acoustic matrix sensor multichannel number
According to acquisition.
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| CN110673081A (en) * | 2019-10-18 | 2020-01-10 | 国网福建省电力有限公司 | Intelligent electric meter error online estimation method based on edge calculation |
| CN110672950A (en) * | 2019-10-08 | 2020-01-10 | 深圳海岸语音技术有限公司 | Power equipment fault sound image detection system and method |
| CN110769032A (en) * | 2019-09-18 | 2020-02-07 | 国网江苏省电力有限公司 | System and method for rapidly detecting and maintaining power grid equipment fault |
| CN111031107A (en) * | 2019-11-29 | 2020-04-17 | 武汉智菱物联科技有限公司 | Geological disaster monitoring system and method based on low-power-consumption communication network |
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| CN111693135A (en) * | 2020-06-03 | 2020-09-22 | 国网宁夏电力有限公司营销服务中心(国网宁夏电力有限公司计量中心) | Power equipment anomaly detection method based on acoustic array edge calculation |
| CN113588141A (en) * | 2020-06-29 | 2021-11-02 | 沈阳中科博微科技股份有限公司 | Collecting and diagnosing working method of capacitive edge computing pressure transmitter |
| CN113588141B (en) * | 2020-06-29 | 2023-03-28 | 沈阳中科博微科技股份有限公司 | Collecting and diagnosing working method of capacitive edge computing pressure transmitter |
| CN112953017A (en) * | 2021-03-29 | 2021-06-11 | 国网新疆电力有限公司阿克苏供电公司 | Comprehensive linkage monitoring device and method for distributed power distribution equipment |
| CN112953017B (en) * | 2021-03-29 | 2023-06-20 | 国网新疆电力有限公司阿克苏供电公司 | Comprehensive linkage monitoring device and monitoring method for distributed power distribution equipment |
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