CN116560344A - Cloud platform-based remote fault diagnosis system for power grid equipment - Google Patents
Cloud platform-based remote fault diagnosis system for power grid equipment Download PDFInfo
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- 238000013500 data storage Methods 0.000 claims description 32
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- 238000013135 deep learning Methods 0.000 claims description 8
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0259—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterized by the response to fault detection
- G05B23/0262—Confirmation of fault detection, e.g. extra checks to confirm that a failure has indeed occurred
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/20—Pc systems
- G05B2219/24—Pc safety
- G05B2219/24065—Real time diagnostics
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
- Y04S10/52—Outage or fault management, e.g. fault detection or location
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Abstract
The invention discloses a cloud platform-based power grid equipment remote fault diagnosis system which comprises a main remote control end, a secondary remote control end, a data acquisition end and an equipment operation end, wherein the data acquisition end is respectively connected with equipment in different areas through data interfaces, and is used for acquiring running state data representing the equipment in real time and transmitting the acquired running state data to a communication module; the communication module sends information to a primary judgment diagnosis module in the secondary remote control end, and the primary judgment diagnosis module is used for receiving equipment fault data, performing primary diagnosis and normalization processing on the received equipment fault data, and transmitting the characteristic parameters which are subjected to primary diagnosis and display faults to the main remote control end; and the main remote control end performs advanced diagnosis on the characteristic parameters with faults. The system can solve the problem that the monitoring and diagnosis of the traditional equipment fault diagnosis mode cannot obtain accurate results, and misjudgment or missed detection frequently occurs.
Description
Technical Field
The invention belongs to the technical field of remote equipment management, and particularly relates to a cloud platform-based power grid equipment remote fault diagnosis system.
Background
Along with the continuous improvement of the automation level of various industries and the wide application of big data technology, a large amount of automation equipment is generally purchased in the production field in industrial production so as to ensure the production efficiency, reduce the production cost and ensure the product quality. The power equipment mainly comprises two major types of power generation equipment and power supply equipment, wherein the power generation equipment mainly comprises a power station boiler, a steam turbine, a gas turbine, a water turbine, a generator, a transformer and the like, and the power supply equipment mainly comprises power transmission lines, transformers, contactors and the like with various voltage levels. However, in practical industrial production, these automation devices are often distributed in different areas and are located at a relatively long distance, even in a location that is difficult to reach manually, and the difficulty in detecting and maintaining the faults of the devices is increased due to the randomness, diversity and complexity of the faults of the devices.
The traditional equipment fault monitoring and diagnosis usually comprises the steps of manually checking specific target equipment one by one, judging the running condition of the equipment through the characteristic parameters of the automatic equipment representing fault information, but the accurate result can not be obtained only through monitoring and diagnosing the single characteristic parameter, and misjudgment or missed detection can often occur. Meanwhile, the monitoring of the automation equipment distributed in different areas has the problems of large data information quantity and low data analysis speed, and a plurality of faults cannot be diagnosed at the same time. In addition, if the on-line monitoring device of a certain device fails, the on-line monitoring device can be normally used only after being repaired or replaced, and the failure can not be continuously diagnosed, so that the failure diagnosis difficulty is further increased.
Therefore, it is necessary to design a fault technique capable of remotely judging a device on line.
Disclosure of Invention
The invention overcomes the defects of the prior art and provides a cloud platform-based remote fault diagnosis system for power grid equipment. The aim of the invention is achieved by the following technical scheme:
the invention provides a cloud platform-based power grid equipment remote fault diagnosis system, which comprises the following components: the device comprises a main remote control end, a secondary remote control end, a data acquisition end and a device operation end;
the data acquisition end is respectively connected with the equipment in different areas through the data interfaces, and is used for acquiring the running state data representing the equipment in real time and transmitting the acquired running state data to the communication module; the communication module sends information to a primary judgment diagnosis module in the secondary remote control end, and the primary judgment diagnosis module is used for receiving equipment fault data, performing primary diagnosis and normalization processing on the received equipment fault data, and transmitting the equipment fault data with faults displayed after primary diagnosis to the main remote control end; and the main remote control end performs advanced diagnosis on the equipment fault data displayed with faults.
Preferably, the main remote control terminal includes:
the central control module is used for receiving primary fault diagnosis sent by the secondary remote control end and transmitting control instructions to the data storage module and the alarm module;
the early warning module is used for sending a periodic fault diagnosis signal to the equipment requiring the equipment operation end to the central control module according to the fault data in the data storage module;
a data storage module for storing equipment failure data subjected to primary diagnosis and advanced diagnosis;
the alarm module is used for giving an alarm to the characteristic parameters which are displayed to have faults after advanced diagnosis;
and the diagnosis module is used for performing advanced diagnosis on the equipment fault data which is displayed to have faults through primary diagnosis according to the parameter advanced operation instruction.
Preferably, the secondary remote control terminal includes:
the secondary center control module is used for packaging the control instruction and the equipment fault data acquisition instruction stored in the secondary data storage module according to an equipment communication protocol and transmitting the packaged control instruction and the equipment fault data acquisition instruction to the data acquisition end through the gateway;
the secondary task allocation module is used for sending a data reading instruction to the secondary data storage module and allocating the equipment fault data and the parameter primary operation instruction stored in the secondary data storage module to the parameter primary diagnosis module;
the secondary data storage module is used for storing equipment fault data acquisition instructions and parameter primary operation instructions configured by the remote server for each equipment and storing the acquired equipment fault data information;
the primary judgment diagnosis module is used for carrying out primary equipment fault data operation on the equipment according to the parameter primary operation instruction and carrying out normalization processing on operation results.
Preferably, the data acquisition end includes:
the communication module is used for intensively transmitting the fault backup data of the automation equipment collected in the sites of different areas to the secondary data storage module of the secondary remote control end for storage;
the data acquisition module is used for transmitting equipment fault data acquisition instructions to acquisition equipment, receiving the equipment fault data transmitted by the acquisition equipment in real time and transmitting the received equipment fault data to the communication module;
the power module is used for providing working power for the data acquisition module, the communication module and the data management module;
and the data management module is used for storing the equipment fault data which are transmitted by the data acquisition module in real time and carrying out encryption storage on the equipment fault data, so that the data are prevented from being manually tampered by the main remote control end and the secondary remote control end.
Preferably, the device operation end includes:
each collecting device is correspondingly connected with one device through a data interface, and the collecting device is used for collecting device fault data of the corresponding device; each acquisition device is connected with the data acquisition module through a gateway; the data acquisition module is electrically connected with the communication module.
Preferably, the primary judging and diagnosing module transmits the equipment fault data which is diagnosed as no fault for the first time to a data storage module of the main remote control end for storage; and transmitting the equipment fault data which is judged to be faulty after diagnosis to a main remote control end for further advanced diagnosis.
Preferably, the acquisition module comprises an acquisition circuit, and the acquisition circuit comprises a threshold module, a comparison conversion module, an output holding module and an FPGA module which are sequentially connected.
More preferably, the input end of the threshold module is respectively connected with a resistor R5, a resistor R6 and a resistor R7; the input end of the comparison conversion module is also connected with a logic circuit, the logic circuit comprises a resistor R1, a resistor R2, a resistor R3, a resistor R4, a resistor R8, a diode D1, a diode D2, a switch K1 and a switch K2, the diode D1 is connected with the resistor R1 in series, the switch K1 is connected with the diode D2 in series, the resistor R8 is connected with the switch K2 in series, one end of the switch K2 is grounded, the resistor R4 is connected with the resistor R3 in series, one end of the resistor R4 is grounded, the resistor R2 is respectively connected with the resistor R3 and the resistor R1 in parallel, and the resistor R1 is respectively connected with the switch K1 and the switch K2 in parallel.
More preferably, the secondary task allocation module adopts a deep learning algorithm to allocate the equipment fault data, and the equipment fault data and the parameter primary operation instruction are in one-to-one correspondence to form a task matrix and then transmitted to the parameter primary diagnosis module for operation.
More preferably, the deep learning algorithm step includes the following steps:
s1, classifying and dividing labels of fault data of existing equipment;
s2, carrying out normalization processing on the divided equipment fault data;
s3, extracting features of the normalized equipment fault data by using a GRU model;
s4, initializing training parameters of the GRU model and training the GRU model;
s5, inputting the real-time equipment fault data into the GRU model to obtain a task matrix.
According to the cloud platform power grid equipment fault remote diagnosis system, equipment of different equipment in different areas is collected in a concentrated mode, primary diagnosis and advanced diagnosis are carried out, and efficient and accurate monitoring and fault diagnosis on the operation state of the remote equipment can be achieved through double judgment, so that the problems that accurate results cannot be obtained in traditional equipment fault diagnosis mode monitoring and diagnosis, misjudgment or missed detection frequently occur, and the problems that data information amount is large, data analysis speed is low and multiple equipment faults cannot be diagnosed simultaneously in traditional mode monitoring are solved; the invention can realize the hierarchical storage of the data, the data transmitted by the faulty equipment can be further uploaded and stored after advanced diagnosis, the accuracy of fault diagnosis is improved, the data storage is carried out at the equipment level and the primary level, and the data of the equipment is prevented from being tampered; the acquisition circuit has the advantages of low power consumption, strong reusability, small volume and flexible cutting, and can efficiently acquire the relevant data of the equipment; task management is distributed through a deep learning algorithm, so that time is utilized more efficiently and reasonably, and the aim of solving the problem more quickly is fulfilled.
Drawings
The invention will be further described with reference to the accompanying drawings, in which embodiments do not constitute any limitation of the invention, and other drawings can be obtained by one of ordinary skill in the art without inventive effort from the following drawings.
FIG. 1 is a frame diagram of a cloud platform-based remote fault diagnosis system for power grid equipment of the present invention;
fig. 2 is a circuit schematic of the acquisition circuit of the present invention.
Detailed Description
A remote fault diagnosis system for a cloud platform-based power grid apparatus is described in further detail below with reference to specific embodiments, which are for comparison and explanation purposes only, and the present invention is not limited to these embodiments.
In an embodiment, as shown in fig. 1, a cloud platform-based remote fault diagnosis system for power grid equipment is provided, which includes: the device comprises a main remote control end, a secondary remote control end, a data acquisition end and a device operation end;
the data acquisition end is respectively connected with the equipment in different areas through the data interfaces, and is used for acquiring the running state data representing the equipment in real time and transmitting the acquired running state data to the communication module; the communication module sends information to a primary judgment diagnosis module in the secondary remote control end, and the primary judgment diagnosis module is used for receiving equipment fault data, performing primary diagnosis and normalization processing on the received equipment fault data, and transmitting the equipment fault data with faults displayed after primary diagnosis to the main remote control end; and the main remote control end performs advanced diagnosis on the equipment fault data displayed with faults.
Preferably, the main remote control terminal includes:
the central control module is used for receiving primary fault diagnosis sent by the secondary remote control end and transmitting control instructions to the data storage module and the alarm module;
the early warning module is used for sending a periodic fault diagnosis signal to the equipment requiring the equipment operation end to the central control module according to the fault data in the data storage module;
a data storage module for storing equipment failure data subjected to primary diagnosis and advanced diagnosis;
the alarm module is used for giving an alarm to the characteristic parameters which are displayed to have faults after advanced diagnosis;
and the diagnosis module is used for performing advanced diagnosis on the equipment fault data which is displayed to have faults through primary diagnosis according to the parameter advanced operation instruction.
Preferably, the secondary remote control terminal includes:
the secondary center control module is used for packaging the control instruction and the equipment fault data acquisition instruction stored in the secondary data storage module according to an equipment communication protocol and transmitting the packaged control instruction and the equipment fault data acquisition instruction to the data acquisition end through the gateway;
the secondary task allocation module is used for sending a data reading instruction to the secondary data storage module and allocating the equipment fault data and the parameter primary operation instruction stored in the secondary data storage module to the parameter primary diagnosis module;
the secondary data storage module is used for storing equipment fault data acquisition instructions and parameter primary operation instructions configured by the remote server for each equipment and storing the acquired equipment fault data information;
the primary judgment diagnosis module is used for carrying out primary equipment fault data operation on the equipment according to the parameter primary operation instruction and carrying out normalization processing on operation results.
Preferably, the data acquisition end includes:
the communication module is used for intensively transmitting the fault backup data of the automation equipment collected in the sites of different areas to the secondary data storage module of the secondary remote control end for storage;
the data acquisition module is used for transmitting equipment fault data acquisition instructions to acquisition equipment, receiving the equipment fault data transmitted by the acquisition equipment in real time and transmitting the received equipment fault data to the communication module;
the power module is used for providing working power for the data acquisition module, the communication module and the data management module;
the power module applies uninterruptible power supply technology, and can ensure that data and data are not lost at the moment of power failure, thereby ensuring the stable and continuous operation of the system.
And the data management module is used for storing the equipment fault data which are transmitted by the data acquisition module in real time and carrying out encryption storage on the equipment fault data, so that the data are prevented from being manually tampered by the main remote control end and the secondary remote control end.
Preferably, the device operation end includes:
each collecting device is correspondingly connected with one device through a data interface, and the collecting device is used for collecting device fault data of the corresponding device; each acquisition device is connected with the data acquisition module through a gateway; the data acquisition module is electrically connected with the communication module.
In this embodiment, the collection device may be an infrared sensor, a collection chip or a manual input terminal, and the corresponding collection device is set corresponding to the relevant power grid device, and the key data is collected and transmitted to the main remote control end and the secondary remote control end to perform fault judgment.
Preferably, the primary judging and diagnosing module transmits the equipment fault data which is diagnosed as no fault for the first time to a data storage module of the main remote control end for storage; and transmitting the equipment fault data which is judged to be faulty after diagnosis to a main remote control end for further advanced diagnosis.
The fault cause can be judged more accurately through a double-layer judging mechanism of the main remote control end and the secondary remote control end, related nearby staff is informed to repair the fault before through the alarm module, when the two fault judgments are different, related data are repaired according to the investigation of the on-site staff and are input into a secondary task allocation module database comprising deep learning, and the judging standard is updated.
In addition, the system comprises a three-layer data storage mechanism, because the stored historical data are shared, idle calculation and storage resources are convenient to integrate, the storage space is more effectively optimized, faulty data are sent to a main remote control end in a secondary data storage module to carry out advanced fault judgment, corresponding equipment ends can record, the risk of changing the data in a single direction can be avoided, and the data without faults are uploaded to a data storage module of the main remote control end of the cloud platform to be stored.
Preferably, the acquisition module comprises an acquisition circuit, and the acquisition circuit comprises a threshold module, a comparison conversion module, an output holding module and an FPGA module which are sequentially connected.
More preferably, the input end of the threshold module is respectively connected with a resistor R5, a resistor R6 and a resistor R7; the input end of the comparison conversion module is also connected with a logic circuit, the logic circuit comprises a resistor R1, a resistor R2, a resistor R3, a resistor R4, a resistor R8, a diode D1, a diode D2, a switch K1 and a switch K2, the diode D1 is connected with the resistor R1 in series, the switch K1 is connected with the diode D2 in series, the resistor R8 is connected with the switch K2 in series, one end of the switch K2 is grounded, the resistor R4 is connected with the resistor R3 in series, one end of the resistor R4 is grounded, the resistor R2 is respectively connected with the resistor R3 and the resistor R1 in parallel, and the resistor R1 is respectively connected with the switch K1 and the switch K2 in parallel.
As shown in FIG. 2, for the acquisition circuit of the present invention, the acquisition circuit of the present system can satisfy the use environment of various discrete amount acquisition cycle tasks, and the peripheral port configuration can flexibly implement the ground/open discrete amounts. Compared with the traditional acquisition circuit, the circuit has the advantages of low power consumption, strong reusability, small volume, high acquisition progress, flexible cutting and the like, is successfully applied to a real environment, and has accurate and reliable acquisition function through test verification.
More preferably, the secondary task allocation module adopts a deep learning algorithm to allocate the equipment fault data, and the equipment fault data and the parameter primary operation instruction are in one-to-one correspondence to form a task matrix and then transmitted to the parameter primary diagnosis module for operation.
More preferably, the deep learning algorithm step includes the following steps:
s1, classifying and dividing labels of fault data of existing equipment;
s2, carrying out normalization processing on the divided equipment fault data;
s3, extracting features of the normalized equipment fault data by using a GRU model;
s4, initializing training parameters of the GRU model and training the GRU model;
s5, inputting the real-time equipment fault data into the GRU model to obtain a task matrix.
S3, extracting preset data and vectorizing the data, inputting the vectorized data into a GRU model, and introducing an attention mechanism, wherein the attention mechanism refers to a mask mechanism, a minimum value is given to the position of complement 0, and the hidden layer state generates other related data through a tanh activation function on linear transformation and is embedded into the vectorized data;
in S4, the weighted cross entropy is used as a loss function to solve the problem of sample imbalance, and weights are assigned to different label classes, so that more attention is given to labels with smaller sample size, and the loss function loss is as follows:
wherein w (c) j ) Is a loss weight; i c For loss weight and failure category c j The proportion of fault data in the system is adjusted by a constant alpha;representing an actual fault label, wherein L represents the data size, C represents fault data, and j and i are vectors;
then putting the data set information processed in the step S2 into a GRU model in the step S3 to extract features, and training the GRU model according to the GRU model training parameters set in the step S4;
and S5, placing the equipment fault data to be tested into the trained GRU model in S5, and finally obtaining a task matrix.
The early warning module can perform operation simulation on the data in the data storage module, and realize digital virtual-real mutual judgment, so that remote pre-diagnosis on equipment faults is realized.
The diagnosis module can be AI software on-line diagnosis, expert system fault diagnosis and the like or can be manually judged by setting a terminal window.
Finally, it should be noted that the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the scope of the present invention, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions can be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.
Claims (10)
1. The utility model provides a remote fault diagnosis system of power grid equipment based on cloud platform which characterized in that includes the connection in proper order: the device comprises a main remote control end, a secondary remote control end, a data acquisition end and a device operation end;
the data acquisition end is respectively connected with the equipment in different areas through the data interfaces, and is used for acquiring the running state data representing the equipment in real time and transmitting the acquired running state data to the communication module; the communication module sends information to a primary judgment diagnosis module in the secondary remote control end, and the primary judgment diagnosis module is used for receiving equipment fault data, performing primary diagnosis and normalization processing on the received equipment fault data, and transmitting the equipment fault data with faults displayed after primary diagnosis to the main remote control end; and the main remote control end performs advanced diagnosis on the equipment fault data displayed with faults.
2. The cloud platform-based power grid equipment remote fault diagnosis system according to claim 1, wherein the main remote control terminal comprises:
the central control module is used for receiving primary fault diagnosis sent by the secondary remote control end and transmitting control instructions to the data storage module and the alarm module;
the early warning module is used for sending a periodic fault diagnosis signal to the equipment requiring the equipment operation end to the central control module according to the fault data in the data storage module;
a data storage module for storing equipment failure data subjected to primary diagnosis and advanced diagnosis;
the alarm module is used for giving an alarm to the characteristic parameters which are displayed to have faults after advanced diagnosis;
and the diagnosis module is used for performing advanced diagnosis on the equipment fault data which is displayed to have faults through primary diagnosis according to the parameter advanced operation instruction.
3. The cloud platform-based power grid equipment remote fault diagnosis system according to claim 1, wherein the secondary remote control terminal comprises:
the secondary center control module is used for packaging the control instruction and the equipment fault data acquisition instruction stored in the secondary data storage module according to an equipment communication protocol and transmitting the packaged control instruction and the equipment fault data acquisition instruction to the data acquisition end through the gateway;
the secondary task allocation module is used for sending a data reading instruction to the secondary data storage module and allocating the equipment fault data and the parameter primary operation instruction stored in the secondary data storage module to the parameter primary diagnosis module;
the secondary data storage module is used for storing equipment fault data acquisition instructions and parameter primary operation instructions configured by the remote server for each equipment and storing the acquired equipment fault data information;
the primary judgment diagnosis module is used for carrying out primary equipment fault data operation on the equipment according to the parameter primary operation instruction and carrying out normalization processing on operation results.
4. The cloud platform-based power grid equipment remote fault diagnosis system according to claim 1, wherein the data acquisition end comprises:
the communication module is used for intensively transmitting the fault backup data of the automation equipment collected in the sites of different areas to the secondary data storage module of the secondary remote control end for storage;
the data acquisition module is used for transmitting equipment fault data acquisition instructions to acquisition equipment, receiving the equipment fault data transmitted by the acquisition equipment in real time and transmitting the received equipment fault data to the communication module;
the power module is used for providing working power for the data acquisition module, the communication module and the data management module;
and the data management module is used for storing the equipment fault data which are transmitted by the data acquisition module in real time and carrying out encryption storage on the equipment fault data, so that the data are prevented from being manually tampered by the main remote control end and the secondary remote control end.
5. The cloud platform-based power grid equipment remote fault diagnosis system according to claim 1, wherein the equipment operation end comprises:
each collecting device is correspondingly connected with one device through a data interface, and the collecting device is used for collecting device fault data of the corresponding device; each acquisition device is connected with the data acquisition module through a gateway; the data acquisition module is electrically connected with the communication module.
6. The cloud platform-based power grid equipment remote fault diagnosis system according to claim 3, wherein the primary judgment diagnosis module transmits equipment fault data which is diagnosed as no fault for the first time to a data storage module of a main remote control end for storage; and transmitting the equipment fault data which is judged to be faulty after diagnosis to a main remote control end for further advanced diagnosis.
7. The cloud platform-based power grid equipment remote fault diagnosis system according to claim 1, wherein the acquisition module comprises an acquisition circuit, and the acquisition circuit comprises a threshold module, a comparison conversion module, an output holding module and an FPGA module which are sequentially connected.
8. The cloud platform-based power grid equipment remote fault diagnosis system according to claim 7, wherein the input end of the threshold module is respectively connected with a resistor R5, a resistor R6 and a resistor R7; the input end of the comparison conversion module is also connected with a logic circuit, the logic circuit comprises a resistor R1, a resistor R2, a resistor R3, a resistor R4, a resistor R8, a diode D1, a diode D2, a switch K1 and a switch K2, the diode D1 is connected with the resistor R1 in series, the switch K1 is connected with the diode D2 in series, the resistor R8 is connected with the switch K2 in series, one end of the switch K2 is grounded, the resistor R4 is connected with the resistor R3 in series, one end of the resistor R4 is grounded, the resistor R2 is respectively connected with the resistor R3 and the resistor R1 in parallel, and the resistor R1 is respectively connected with the switch K1 and the switch K2 in parallel.
9. The cloud platform-based power grid equipment remote fault diagnosis system according to claim 3, wherein the secondary task allocation module adopts a deep learning algorithm to allocate equipment fault data, and the equipment fault data and parameter primary operation instructions are formed into a task matrix in a one-to-one correspondence mode and then transmitted to the parameter primary diagnosis module for operation.
10. The cloud platform based grid appliance remote fault diagnosis system of claim 9, wherein the deep learning algorithm step comprises the following steps:
s1, classifying and dividing labels of fault data of existing equipment;
s2, carrying out normalization processing on the divided equipment fault data;
s3, extracting features of the normalized equipment fault data by using a GRU model;
s4, initializing training parameters of the GRU model and training the GRU model;
s5, inputting the real-time equipment fault data into the GRU model to obtain a task matrix.
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| CN217766594U (en) * | 2022-07-15 | 2022-11-08 | 青岛中车华轩水务有限公司 | Sewage treatment fault monitoring system based on Internet of things |
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