CN117150414B - Fault diagnosis method - Google Patents
Fault diagnosis method Download PDFInfo
- Publication number
- CN117150414B CN117150414B CN202311348016.7A CN202311348016A CN117150414B CN 117150414 B CN117150414 B CN 117150414B CN 202311348016 A CN202311348016 A CN 202311348016A CN 117150414 B CN117150414 B CN 117150414B
- Authority
- CN
- China
- Prior art keywords
- fault
- power supply
- data
- diagnosis
- unit
- 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
- 238000003745 diagnosis Methods 0.000 title claims abstract description 161
- 238000000034 method Methods 0.000 title claims abstract description 37
- 238000012544 monitoring process Methods 0.000 claims abstract description 51
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 49
- 238000012423 maintenance Methods 0.000 claims abstract description 34
- 230000008439 repair process Effects 0.000 claims abstract description 34
- 238000005516 engineering process Methods 0.000 claims abstract description 27
- 238000007781 pre-processing Methods 0.000 claims abstract description 14
- 238000004088 simulation Methods 0.000 claims abstract description 11
- 230000005540 biological transmission Effects 0.000 claims abstract description 10
- 238000000605 extraction Methods 0.000 claims abstract description 7
- 238000012549 training Methods 0.000 claims abstract description 7
- 238000012545 processing Methods 0.000 claims description 30
- 230000002159 abnormal effect Effects 0.000 claims description 27
- 230000007613 environmental effect Effects 0.000 claims description 26
- 238000004891 communication Methods 0.000 claims description 24
- 238000001514 detection method Methods 0.000 claims description 18
- 238000010801 machine learning Methods 0.000 claims description 15
- 230000007246 mechanism Effects 0.000 claims description 15
- 238000001914 filtration Methods 0.000 claims description 13
- 230000002265 prevention Effects 0.000 claims description 13
- 238000013135 deep learning Methods 0.000 claims description 11
- 238000004458 analytical method Methods 0.000 claims description 10
- 238000007405 data analysis Methods 0.000 claims description 10
- 230000006870 function Effects 0.000 claims description 10
- 230000017525 heat dissipation Effects 0.000 claims description 9
- 238000013500 data storage Methods 0.000 claims description 8
- 238000012300 Sequence Analysis Methods 0.000 claims description 7
- 230000009466 transformation Effects 0.000 claims description 7
- 239000003990 capacitor Substances 0.000 claims description 6
- 238000013480 data collection Methods 0.000 claims description 6
- 239000000284 extract Substances 0.000 claims description 6
- 238000013507 mapping Methods 0.000 claims description 6
- 230000001960 triggered effect Effects 0.000 claims description 6
- 238000009423 ventilation Methods 0.000 claims description 6
- 238000007418 data mining Methods 0.000 claims description 4
- 230000008569 process Effects 0.000 claims description 4
- 238000013179 statistical model Methods 0.000 claims description 4
- 238000013068 supply chain management Methods 0.000 claims description 4
- 238000012795 verification Methods 0.000 claims description 4
- 208000025274 Lightning injury Diseases 0.000 claims description 3
- 230000032683 aging Effects 0.000 claims description 3
- 230000006399 behavior Effects 0.000 claims description 3
- 230000008859 change Effects 0.000 claims description 3
- 238000004140 cleaning Methods 0.000 claims description 3
- 238000013527 convolutional neural network Methods 0.000 claims description 3
- 238000007689 inspection Methods 0.000 claims description 3
- 230000007787 long-term memory Effects 0.000 claims description 3
- 230000007257 malfunction Effects 0.000 claims description 3
- 238000010606 normalization Methods 0.000 claims description 3
- 238000013021 overheating Methods 0.000 claims description 3
- 230000000737 periodic effect Effects 0.000 claims description 3
- 239000000941 radioactive substance Substances 0.000 claims description 3
- 238000007637 random forest analysis Methods 0.000 claims description 3
- 230000002787 reinforcement Effects 0.000 claims description 3
- 230000006403 short-term memory Effects 0.000 claims description 3
- 239000003381 stabilizer Substances 0.000 claims description 3
- 238000005728 strengthening Methods 0.000 claims description 3
- 238000012706 support-vector machine Methods 0.000 claims description 3
- 230000000007 visual effect Effects 0.000 claims description 3
- 238000013473 artificial intelligence Methods 0.000 abstract description 5
- 230000008030 elimination Effects 0.000 abstract description 4
- 238000003379 elimination reaction Methods 0.000 abstract description 4
- 238000011156 evaluation Methods 0.000 abstract 1
- 230000003449 preventive effect Effects 0.000 description 4
- 238000010586 diagram Methods 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- 238000013024 troubleshooting Methods 0.000 description 2
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/243—Classification techniques relating to the number of classes
- G06F18/2433—Single-class perspective, e.g. one-against-all classification; Novelty detection; Outlier detection
-
- 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
- G01—MEASURING; TESTING
- G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
- G01J5/00—Radiation pyrometry, e.g. infrared or optical thermometry
- G01J5/0096—Radiation pyrometry, e.g. infrared or optical thermometry for measuring wires, electrical contacts or electronic systems
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M13/00—Testing of machine parts
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R19/00—Arrangements for measuring currents or voltages or for indicating presence or sign thereof
- G01R19/165—Indicating that current or voltage is either above or below a predetermined value or within or outside a predetermined range of values
- G01R19/16533—Indicating that current or voltage is either above or below a predetermined value or within or outside a predetermined range of values characterised by the application
- G01R19/16538—Indicating that current or voltage is either above or below a predetermined value or within or outside a predetermined range of values characterised by the application in AC or DC supplies
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R19/00—Arrangements for measuring currents or voltages or for indicating presence or sign thereof
- G01R19/165—Indicating that current or voltage is either above or below a predetermined value or within or outside a predetermined range of values
- G01R19/16566—Circuits and arrangements for comparing voltage or current with one or several thresholds and for indicating the result not covered by subgroups G01R19/16504, G01R19/16528, G01R19/16533
- G01R19/16571—Circuits and arrangements for comparing voltage or current with one or several thresholds and for indicating the result not covered by subgroups G01R19/16504, G01R19/16528, G01R19/16533 comparing AC or DC current with one threshold, e.g. load current, over-current, surge current or fault current
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R19/00—Arrangements for measuring currents or voltages or for indicating presence or sign thereof
- G01R19/165—Indicating that current or voltage is either above or below a predetermined value or within or outside a predetermined range of values
- G01R19/16566—Circuits and arrangements for comparing voltage or current with one or several thresholds and for indicating the result not covered by subgroups G01R19/16504, G01R19/16528, G01R19/16533
- G01R19/16576—Circuits and arrangements for comparing voltage or current with one or several thresholds and for indicating the result not covered by subgroups G01R19/16504, G01R19/16528, G01R19/16533 comparing DC or AC voltage with one threshold
-
- 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
- G01R31/40—Testing power supplies
-
- 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
- G01R31/50—Testing of electric apparatus, lines, cables or components for short-circuits, continuity, leakage current or incorrect line connections
- G01R31/52—Testing for short-circuits, leakage current or ground faults
-
- 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
- G01R31/50—Testing of electric apparatus, lines, cables or components for short-circuits, continuity, leakage current or incorrect line connections
- G01R31/74—Testing of fuses
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/14—Fourier, Walsh or analogous domain transformations, e.g. Laplace, Hilbert, Karhunen-Loeve, transforms
- G06F17/141—Discrete Fourier transforms
- G06F17/142—Fast Fourier transforms, e.g. using a Cooley-Tukey type algorithm
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/10—Pre-processing; Data cleansing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/213—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F8/00—Arrangements for software engineering
- G06F8/30—Creation or generation of source code
- G06F8/38—Creation or generation of source code for implementing user interfaces
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00001—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by the display of information or by user interaction, e.g. supervisory control and data acquisition systems [SCADA] or graphical user interfaces [GUI]
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00002—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by monitoring
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00006—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by information or instructions transport means between the monitoring, controlling or managing units and monitored, controlled or operated power network element or electrical equipment
- H02J13/00022—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by information or instructions transport means between the monitoring, controlling or managing units and monitored, controlled or operated power network element or electrical equipment using wireless data transmission
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00006—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by information or instructions transport means between the monitoring, controlling or managing units and monitored, controlled or operated power network element or electrical equipment
- H02J13/00022—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by information or instructions transport means between the monitoring, controlling or managing units and monitored, controlled or operated power network element or electrical equipment using wireless data transmission
- H02J13/00026—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by information or instructions transport means between the monitoring, controlling or managing units and monitored, controlled or operated power network element or electrical equipment using wireless data transmission involving a local wireless network, e.g. Wi-Fi, ZigBee or Bluetooth
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00006—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by information or instructions transport means between the monitoring, controlling or managing units and monitored, controlled or operated power network element or electrical equipment
- H02J13/00028—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by information or instructions transport means between the monitoring, controlling or managing units and monitored, controlled or operated power network element or electrical equipment involving the use of Internet protocols
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Power Engineering (AREA)
- General Engineering & Computer Science (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Artificial Intelligence (AREA)
- Evolutionary Computation (AREA)
- Evolutionary Biology (AREA)
- Life Sciences & Earth Sciences (AREA)
- Bioinformatics & Computational Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Computer Networks & Wireless Communication (AREA)
- Computational Mathematics (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- Pure & Applied Mathematics (AREA)
- Human Computer Interaction (AREA)
- Algebra (AREA)
- Databases & Information Systems (AREA)
- Medical Informatics (AREA)
- Computing Systems (AREA)
- Discrete Mathematics (AREA)
- Spectroscopy & Molecular Physics (AREA)
- Testing And Monitoring For Control Systems (AREA)
Abstract
The invention discloses a fault diagnosis method, which comprises the steps of S1 data acquisition and preprocessing, S2 feature extraction and selection, S3 fault diagnosis model training and evaluation, S4 remote monitoring and diagnosis, S5 hardware integrated diagnosis function, S6 intelligent diagnosis tool and software application, S7 integrated fault prediction function, S8 fault repair and maintenance, S9 data transmission to a mobile terminal, S10 virtual power system simulation environment setting and S11 automatic fault repair; according to the invention, an automatic algorithm and an artificial intelligence technology are introduced to perform automatic fault diagnosis, so that faults are rapidly and accurately positioned, and corresponding solutions are provided; the power supply system is monitored and diagnosed in real time through the internet connection and the remote access technology, so that maintenance personnel can remotely perform fault elimination, the downtime is reduced, and fault information can be obtained in real time so as to be further analyzed and prevented; the user is guided to perform fault diagnosis by developing an intelligent diagnosis tool and a user interface which are easy to use and operate.
Description
Technical Field
The invention relates to the field of power supplies, in particular to a fault diagnosis method.
Background
Fault diagnosis methods play an important role in power fault removal and repair processes, they can help determine the cause of the fault, locate the fault location and provide solutions, but existing power fault diagnosis methods have some drawbacks, firstly, have high complexity, some power fault diagnosis methods may require complex test equipment, instruments and expertise, which make them more difficult to use and operate, are difficult for an ordinary user to grasp, and secondly, require long time consumption, some diagnosis methods may require long time testing and monitoring to determine the cause of the power fault, which may make the fault removal process cumbersome, and may result in excessive system downtime.
Disclosure of Invention
The present invention is directed to a fault diagnosis method, so as to solve the technical difficulties of the prior art that the power fault diagnosis method still has higher complexity and needs to be repaired for a longer time.
In order to achieve the above purpose, the present invention provides the following technical solutions:
a fault diagnosis method comprising the steps of:
s1, installing and configuring a sensor and a measuring device, wherein the sensor and the measuring device are used for collecting power performance data, and preprocessing the collected data, including filtering, denoising and normalizing;
S2, extracting features from the preprocessed data, wherein the features comprise features of time domain, frequency domain and wavelet transformation, and selecting the features with the most information quantity for subsequent fault diagnosis according to the importance degree of the features;
s3, a fault diagnosis model is established by using a machine learning and deep learning method, the model is trained by using marked training data, and model parameters are evaluated and optimized by using a verification set;
s4, configuring a remote communication unit, ensuring that power supply equipment, a cloud platform and a central monitoring system are in real-time communication, transmitting power supply performance data to the cloud platform and the central monitoring system, ensuring the real-time performance and reliability of the data, and providing a remote access interface and a control unit so that a user can remotely monitor and control the power supply system;
s5, integrating a sensor and a fault detection circuit on the power supply equipment, wherein the sensor and the fault detection circuit are used for monitoring the power supply state and detecting abnormal conditions in real time, configuring an alarm device, and sending out corresponding alarm signals when faults or abnormal conditions are detected;
s6, designing a user interface so that a user can input related information, providing an interface which is easy to operate and use, processing and analyzing data input by the user, executing a fault diagnosis algorithm to determine the cause and the position of the fault, visually displaying a diagnosis result to the user, and providing corresponding solutions and suggestions;
S7, collecting and storing historical data, running states and environmental parameter information of the power supply system, analyzing and modeling the historical data by using a data analysis and modeling technology, establishing a power supply fault prediction model, and providing corresponding prevention strategies and measures according to predicted fault risks so as to reduce the probability of future faults;
s8, automatically triggering a work order system of a maintainer according to a prediction result of the fault diagnosis model so as to timely perform fault repair and maintenance work;
s9, transmitting the fault diagnosis model and real-time data to the mobile terminal equipment by using a wireless communication technology, so that maintenance personnel can perform fault diagnosis and repair work at any time and any place;
s10, setting a virtual power system simulation environment, and providing real-time fault diagnosis and repair guidance;
s11, integrating the fault diagnosis model and the prediction model with a supply chain management system to realize automatic spare part allocation and fault repair processes, and improving the operation efficiency of the power supply system and reducing the maintenance cost.
The system comprises an automatic fault diagnosis module, a data acquisition unit, a data preprocessing unit, a feature extraction unit, a fault diagnosis algorithm unit and a result display and report unit, wherein the S1, the S2 and the S3 are supported by the automatic fault diagnosis module;
The data acquisition unit is responsible for collecting power performance data, including voltage, current and power parameters, and the voltage sensor uses the following formula to measure: u=r×i, where U is voltage, R is resistance, and I is current; the data preprocessing unit performs preprocessing operations of filtering, denoising and normalizing on the acquired data so as to facilitate subsequent analysis, the filtering eliminates high-frequency noise and low-frequency noise through a digital filter, the denoising eliminates abnormal values through a signal processing algorithm comprising wavelet transformation and mean value filtering, and the normalization is used for mapping data in different ranges to a unified numerical range; the feature extraction unit extracts useful features from the preprocessed data for input of a fault diagnosis algorithm, and extracts frequency domain features by calculating time domain statistical features of means, variances, peaks and valleys of the signals and converting the signals from the time domain to the frequency domain using a fast fourier transform; the fault diagnosis algorithm unit uses machine learning and deep learning algorithms to judge the fault type and position according to the extracted characteristics, establishes a fault diagnosis model by using a support vector machine, a random forest and a convolutional neural network, and classifies the fault diagnosis model by inputting the characteristics; the results display and reporting unit displays the diagnostic results to maintenance personnel, including presentation in the form of charts, reports, and generates diagnostic reports and suggested solutions.
The S4 is supported by a remote monitoring and processing module, and the remote monitoring and processing module comprises a remote communication unit, a data transmission unit, a remote access interface unit and a remote control unit;
the remote communication unit realizes communication between the power supply system and the remote monitoring equipment through Ethernet, wireless network and Bluetooth connection, and in the Ethernet, the communication between the equipment is realized by using a standard TCP/IP protocol; the data transmission unit transmits the power performance data to the cloud platform and the central monitoring system, so that the effectiveness of real-time monitoring and diagnosis is ensured, and the data is transmitted by using a HTTP, MQTT, webSocket protocol, so that the real-time performance and reliability of the data are ensured; the remote access interface unit provides an interface for a user to remotely access the power supply system and acquire key parameters and fault information, and the user logs in by using a user name and a password and then checks the key parameters and the fault information; the remote control unit allows maintenance personnel to remotely control the power supply system for troubleshooting and repair operations, and control of the device is achieved by using command transmission protocols, including SSH and Telnet.
The S5 is supported by a hardware integrated diagnosis functional module, and the hardware integrated diagnosis functional module comprises a sensor unit, a fault detection circuit unit and an alarm device unit;
The sensor unit integrates related sensors into power equipment, and comprises a voltage sensor, a current sensor and a temperature sensor, wherein key parameters of voltage, current and temperature are monitored; the fault detection circuit unit monitors the power supply state and detects abnormal conditions through a designed fault detection circuit, and the overload protection circuit judges whether overload occurs or not through monitoring whether the current value exceeds the rated range or not; when the alarm device unit is in fault or abnormal condition, the alarm device unit sends out alarm signals of sound and light, including a buzzer, an LED indicator lamp and an alarm triggering notification.
The S6 is supported by an intelligent diagnosis tool and a software application module, wherein the intelligent diagnosis tool and the software application module comprise a user interface display unit, a data input and processing unit, a fault diagnosis algorithm unit and a result display and suggestion unit;
the user interface display unit provides an interface which is easy to use and operate, and comprises a graphical user interface and a command line interface, wherein a user inputs related information comprising equipment names and fault descriptions through the interface; the data input and processing unit guides a user to input related information, processes and analyzes the input data so as to facilitate subsequent diagnosis operation, and converts fault description input by the user into structurable data by using a text analysis technology so as to facilitate the subsequent diagnosis operation; the fault diagnosis algorithm unit executes a fault diagnosis algorithm to determine the cause and the position of the fault according to the data input by the user and the existing knowledge base, and performs fault diagnosis by using an expert system based on a rule reasoning engine and a pattern matching algorithm; the result display and suggestion unit visually presents the diagnosis result to the user and gives corresponding suggestions and solutions.
The S7 is supported by an integrated fault prediction functional module, and the integrated fault prediction functional module comprises a data collection and storage unit, a data analysis and modeling unit, a fault prediction algorithm unit and a prevention strategy and measure execution unit;
the data collection and storage unit collects and stores information of historical data, running states and environmental parameters of the power supply system, and the data storage is carried out by using a database and a cloud platform, so that reliability and expandability are ensured; the data analysis and modeling unit analyzes and models historical data by using the technology of data mining and machine learning to predict potential fault risks, and establishes a power failure prediction model by using a time sequence analysis method comprising an autoregressive moving average model and a long-term and short-term memory network; the fault prediction algorithm unit applies a statistical model, a time sequence analysis and a regression algorithm, performs fault prediction based on a modeling result, and realizes a prediction algorithm by using techniques of supervised learning, unsupervised learning and reinforcement learning; the prevention strategy and measure unit gives corresponding prevention strategies and measures according to the predicted fault risk to reduce the probability of future faults, and the suggested maintenance scheme comprising periodic inspection, part replacement and equipment maintenance is provided in the diagnosis result to reduce the probability of future faults.
The automatic fault diagnosis module, the remote monitoring and processing module, the hardware integrated diagnosis function module, the intelligent diagnosis tool and the software application module can detect power source fault reasons including overload, short circuit, overvoltage, undervoltage, fault protection, temperature problems, power source element faults, environmental interference and error operation;
overload refers to the fact that the power supply load exceeds the rated capacity of the power supply, so that the power supply cannot provide enough electric energy, and the reasons for overload include that the equipment is connected with too many loads, a certain load suddenly increases, voltage drop and current increase of the power supply are caused, and even a power fuse trips; short circuit refers to low impedance connection between the output ends of the power supply or between the output ends of the power supply and the ground, so that excessive current is caused, the short circuit causes damage to wires, connectors and electric elements, and the short circuit causes tripping of a power fuse and starting of a protection mechanism for overload protection of the power supply; overvoltage means that the voltage output by a power supply exceeds the rated value of the power supply, and the reasons include voltage fluctuation of a power grid and failure of the power supply, and the connected equipment is damaged by the excessive voltage, so that the equipment fails; the undervoltage refers to that the voltage output by the power supply is lower than the rated value of the power supply, and the reasons include lower voltage in a power grid, failure of internal components of the power supply, failure of the equipment caused by the undervoltage and unstable power supply; the fault protection means that when a protection mechanism in the power supply detects internal faults and external anomalies of the power supply, the protection mechanism comprising overcurrent protection and overtemperature protection is triggered, and when the protection mechanism is triggered, the power supply stops outputting electric energy so as to prevent further damage and dangerous situations; the temperature problem means that the high temperature environment can cause ageing and overheating of the power supply assembly, so that the efficiency and reliability of the power supply are reduced, and the performance of the power supply is also affected in the low temperature environment; the power supply element fault means that the electronic elements in the power supply, including a capacitor, a transformer and a relay, also can fail, so that the power supply works abnormally; environmental interference refers to the fact that the power supply is affected by environmental interference from other electromagnetic equipment, radioactive substances, electromagnetic waves, electromagnetic pulses and lightning strokes, which can cause interference and damage to the power supply, resulting in power failure; incorrect operation means that incorrect use and operation of the power supply device can lead to malfunctions, including incorrect wiring and incorrect setting of parameters.
The specific method for judging the power failure by the automatic failure diagnosis module, the remote monitoring and processing module, the hardware integrated diagnosis function module, the intelligent diagnosis tool and the software application module comprises the following steps:
monitoring the flicker and color change of the power indicator and the display of error codes of the display screen, and primarily presuming and judging the fault type;
the voltage meter and the ammeter are connected to the output end of the power supply and the key circuit, the values of the voltage and the current are monitored in real time, and abnormal voltage and current values represent the fault types of overload, short circuit, overvoltage and undervoltage;
the fuse and the fuse are common protection devices in the power supply, damage can occur when faults occur, and whether overload and short-circuit faults occur is primarily judged by guiding a user to check whether the fuse and the fuse are blown and burned;
detecting the temperature conditions in and around the power supply equipment by monitoring the data of the infrared thermometer and the sensor;
abnormal noise and vibration indicate the failure of the internal components of the power supply, including the damaged capacitor and fan, and the failure type is primarily determined by analyzing the data of the vibration sensor.
The solution provided for the user after the automatic fault diagnosis module, the remote monitoring and processing module, the hardware integrated diagnosis function module, the intelligent diagnosis tool and the software application module judge the power failure type comprises the following steps:
When the fault type is overload and short circuit, firstly checking the load condition, confirming whether excessive equipment is connected to a power supply, if so, reducing the load and redistributing the load, then checking whether the wires among the power line, the socket and the equipment are loose and damaged, repairing and replacing damaged parts, and finally, if the fuses and the fuses are found to burn out, replacing new fuses and fuses according to the specification;
when the fault type is overvoltage and undervoltage, firstly checking whether the input voltage of the power supply is normal or not, if so, contacting the power supplier for processing, and secondly, for the condition of frequently generating overvoltage and undervoltage, maintaining stable power supply output by using a voltage stabilizer and adjusting constant output parameters of the power supply;
when the fault type is fault protection, firstly, according to a power manual and a user guide, executing corresponding steps to reset a protection mechanism, then checking environmental factors to ensure that the surrounding environment is suitable for normal operation of a power supply, including proper temperature and good ventilation, and finally checking whether internal elements of the power supply are damaged and aged or not, and repairing and replacing the fault elements according to requirements;
when the fault type is a temperature problem, firstly ensuring good heat dissipation, removing plugs, cleaning fans, ensuring enough space to be reserved around power supply equipment for ventilation, secondly strengthening heat dissipation means, and adding heat dissipation devices comprising fans and radiators;
When the fault type is environmental interference, firstly, an interference source is found and the propagation of an interference signal is isolated as much as possible, and secondly, a filter and a suppressor are arranged to reduce the influence of the interference signal;
when the fault type is incorrect operation, the user manual is first carefully read, consulted, to ensure correct operation and setting of the power supply device, and then the relevant parameters of the power supply device are reconfigured according to the user manual to ensure correct operation and use.
Modeling an actual power supply system in the step S11, and mapping the actual power supply system into a virtual environment by using simulation software, wherein the modeling comprises simulating the physical structure, electrical elements, characteristics and behaviors of a control system and sensors of the power supply device; in the virtual power supply system, various faults and abnormal conditions are simulated, including voltage fluctuation, current overload and wire short circuit, and various possible fault conditions are simulated in the virtual environment by adjusting simulation parameters and setting fault scenes; the virtual power supply system can collect various key performance indexes and sensor data in real time, wherein the data comprise voltage, current and power, and the data can be transmitted to a fault diagnosis model through a network; inputting real-time data acquired by a virtual power supply system into a model for analysis and diagnosis by applying an established fault diagnosis model, judging whether a fault occurs or not by the model according to a data mode and characteristics based on machine learning and deep learning and technologies, and positioning the cause and the position of the fault; once the fault is diagnosed, the virtual power system simulation environment provides real-time repair guidance, generates recommended lists of repair steps, repair tools and required spare parts for specific fault types and positions, and the guidance is visually presented to provide visual operation guidance and repair flow.
Compared with the prior art, the invention has the beneficial effects that:
(1) By introducing an automatic algorithm and an artificial intelligence technology to carry out automatic fault diagnosis, the fault is rapidly and accurately positioned, a corresponding solution is provided, and the time for fault maintenance is shortened.
(2) Through internet connection and remote access technology, the real-time monitoring and diagnosis of the power supply system are realized, maintenance personnel can remotely conduct fault elimination, downtime and cost are reduced, and fault information can be obtained in real time so as to be further analyzed and prevented.
(3) By developing intelligent diagnostic tools and user interfaces that are easy to use and operate, a user is provided with a friendly interface that guides the user in diagnosing faults, explaining possible causes, and giving advice or steps to solve the problem.
(4) By collecting and analyzing information such as historical data, running states, environmental parameters and the like of the power supply system, potential fault risks are found in time, preventive measures are taken, and the occurrence rate of future faults is reduced.
Drawings
FIG. 1 is a schematic diagram illustrating steps of a fault diagnosis method according to the present invention;
fig. 2 is a schematic diagram of a system structure of a fault diagnosis method according to the present invention.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples are not representative of all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus consistent with some aspects of the disclosure as detailed in the accompanying claims.
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
As shown in fig. 1 to 2, a fault diagnosis method includes the steps of:
s1, installing and configuring a sensor and a measuring device, wherein the sensor and the measuring device are used for collecting power performance data, and preprocessing the collected data, including filtering, denoising and normalizing;
s2, extracting features from the preprocessed data, wherein the features comprise features of time domain, frequency domain and wavelet transformation, and selecting the features with the most information quantity for subsequent fault diagnosis according to the importance degree of the features;
S3, a fault diagnosis model is established by using a machine learning and deep learning method, the model is trained by using marked training data, and model parameters are evaluated and optimized by using a verification set;
s4, configuring a remote communication unit, ensuring that power supply equipment, a cloud platform and a central monitoring system are in real-time communication, transmitting power supply performance data to the cloud platform and the central monitoring system, ensuring the real-time performance and reliability of the data, and providing a remote access interface and a control unit so that a user can remotely monitor and control the power supply system;
s5, integrating a sensor and a fault detection circuit on the power supply equipment, wherein the sensor and the fault detection circuit are used for monitoring the power supply state and detecting abnormal conditions in real time, configuring an alarm device, and sending out corresponding alarm signals when faults or abnormal conditions are detected;
s6, designing a user interface so that a user can input related information, providing an interface which is easy to operate and use, processing and analyzing data input by the user, executing a fault diagnosis algorithm to determine the cause and the position of the fault, visually displaying a diagnosis result to the user, and providing corresponding solutions and suggestions;
s7, collecting and storing historical data, running states and environmental parameter information of the power supply system, analyzing and modeling the historical data by using a data analysis and modeling technology, establishing a power supply fault prediction model, and providing corresponding prevention strategies and measures according to predicted fault risks so as to reduce the probability of future faults;
S8, automatically triggering a work order system of a maintainer according to a prediction result of the fault diagnosis model so as to timely perform fault repair and maintenance work;
s9, transmitting the fault diagnosis model and real-time data to the mobile terminal equipment by using a wireless communication technology, so that maintenance personnel can perform fault diagnosis and repair work at any time and any place;
s10, setting a virtual power system simulation environment, and providing real-time fault diagnosis and repair guidance;
s11, integrating the fault diagnosis model and the prediction model with a supply chain management system to realize automatic spare part allocation and fault repair processes, and improving the operation efficiency of the power supply system and reducing the maintenance cost.
The S1, S2, and S3 are supported by an automated fault diagnosis module that uses algorithms and artificial intelligence techniques to quickly and accurately locate faults by real-time monitoring and analysis of power performance data, using various techniques including machine learning, deep learning, and expert systems to determine fault type and location, and to give corresponding advice or solutions.
The automatic fault diagnosis module comprises a data acquisition unit, a data preprocessing unit, a feature extraction unit, a fault diagnosis algorithm unit and a result display and report unit, wherein the data acquisition unit is responsible for collecting power performance data, including voltage, current and power parameters, and the voltage sensor is used for measuring by using the following formula: u=r×i, where U is voltage, R is resistance, and I is current; the data preprocessing unit performs preprocessing operations of filtering, denoising and normalizing on the acquired data so as to facilitate subsequent analysis, the filtering eliminates high-frequency noise and low-frequency noise through a digital filter, the denoising eliminates abnormal values through a signal processing algorithm comprising wavelet transformation and mean value filtering, and the normalization is used for mapping data in different ranges to a unified numerical range; the feature extraction unit extracts useful features from the preprocessed data for input of a fault diagnosis algorithm, and extracts frequency domain features by calculating time domain statistical features of means, variances, peaks and valleys of the signals and converting the signals from the time domain to the frequency domain using a fast fourier transform; the fault diagnosis algorithm unit uses machine learning and deep learning algorithms to judge the fault type and position according to the extracted characteristics, establishes a fault diagnosis model by using a support vector machine, a random forest and a convolutional neural network, and classifies the fault diagnosis model by inputting the characteristics; the results display and reporting unit displays the diagnostic results to maintenance personnel, including presentation in the form of charts, reports, and generates diagnostic reports and suggested solutions.
The system is characterized in that the S4 is supported by a remote monitoring and processing module, the remote monitoring and processing module utilizes Internet connection and remote access technology, remote real-time monitoring and diagnosis are realized by connecting a power supply system to a cloud platform and a central monitoring system, and maintenance personnel acquire key parameters and fault information through remote access to the power supply system and conduct remote fault removal, so that downtime and cost are reduced.
The remote monitoring and processing module comprises a remote communication unit, a data transmission unit, a remote access interface unit and a remote control unit, wherein the remote communication unit is connected with a power supply system through a network to realize communication between the power supply system and remote monitoring equipment, the remote monitoring and processing module comprises an Ethernet, a wireless network and Bluetooth, and in the Ethernet, a standard TCP/IP protocol is used for realizing communication between the equipment; the data transmission unit transmits the power performance data to the cloud platform and the central monitoring system, so that the effectiveness of real-time monitoring and diagnosis is ensured, and the data is transmitted by using a HTTP, MQTT, webSocket protocol, so that the real-time performance and reliability of the data are ensured; the remote access interface unit provides an interface for a user to remotely access the power supply system and acquire key parameters and fault information, and the user logs in by using a user name and a password and then checks the key parameters and the fault information; the remote control unit allows maintenance personnel to remotely control the power supply system for troubleshooting and repair operations, and control of the device is achieved by using command transmission protocols, including SSH and Telnet.
The S5 is supported by a hardware integrated diagnosis function module which integrates a fault diagnosis function to the hardware level of the power supply equipment, and comprises the steps of adding a fault detection circuit and sensors in the power supply circuit to monitor the power supply state in real time, wherein the sensors are used for monitoring key parameters of voltage, current and temperature, and sending out an alarm when abnormal conditions are detected to remind a user of corresponding maintenance and treatment.
The hardware integrated diagnosis functional module comprises a sensor unit, a fault detection circuit unit and an alarm device unit, wherein the sensor unit integrates related sensors into power equipment, and comprises a voltage sensor, a current sensor and a temperature sensor, and monitors key parameters of voltage, current and temperature; the fault detection circuit unit monitors the power supply state and detects abnormal conditions through a designed fault detection circuit, and the overload protection circuit judges whether overload occurs or not through monitoring whether the current value exceeds the rated range or not; when the alarm device unit is in fault or abnormal condition, the alarm device unit sends out sound, light or other forms of alarm signals, including a buzzer, an LED indicator lamp and an alarm triggering notice.
The S6 is supported by intelligent diagnostic tools and software application modules that provide easy to use and operate interfaces to guide the user in fault diagnosis, which can guide the user to the correct steps based on graphical user interfaces and command line interfaces, collect information and interpret possible causes of faults, and at the same time, give advice and provide recommended solutions according to the type of fault and the requirements of the specific equipment.
The intelligent diagnosis tool and the software application module comprise a user interface display unit, a data input and processing unit, a fault diagnosis algorithm unit and a result display and suggestion unit, wherein the user interface display unit provides interfaces which are easy to use and operate, the interfaces comprise a graphical user interface and a command line interface, and a user inputs related information comprising equipment names and fault descriptions through the interfaces; the data input and processing unit guides a user to input related information, processes and analyzes the input data so as to facilitate subsequent diagnosis operation, and converts fault description input by the user into structurable data by using a text analysis technology so as to facilitate the subsequent diagnosis operation; the fault diagnosis algorithm unit executes a fault diagnosis algorithm to determine the cause and the position of the fault according to the data input by the user and the existing knowledge base, and performs fault diagnosis by using an expert system based on a rule reasoning engine and a pattern matching algorithm; the result display and suggestion unit visually presents the diagnosis result to the user and gives corresponding suggestions and solutions.
The S7 is supported by an integrated fault prediction function module, wherein the integrated fault prediction function module predicts future fault risks by collecting and analyzing information of historical data, running states and environmental parameters of a power supply system and utilizing data mining and machine learning technologies, judges potential fault trends by using a statistical model, time sequence analysis, a regression algorithm and an artificial neural network method, and adopts proper preventive measures to reduce the possibility of occurrence of the future faults.
The integrated fault prediction functional module comprises a data collection and storage unit, a data analysis and modeling unit, a fault prediction algorithm unit and a prevention strategy and measure execution unit, wherein the data collection and storage unit collects and stores information of historical data, running states and environmental parameters of a power supply system, and ensures reliability and expandability by using a database and a cloud platform to store data; the data analysis and modeling unit analyzes and models historical data by using the technology of data mining and machine learning to predict potential fault risks, and establishes a power failure prediction model by using a time sequence analysis method comprising an autoregressive moving average model and a long-term and short-term memory network; the fault prediction algorithm unit applies a statistical model, a time sequence analysis and a regression algorithm, performs fault prediction based on a modeling result, and realizes a prediction algorithm by using techniques of supervised learning, unsupervised learning and reinforcement learning; the prevention strategy and measure unit gives corresponding prevention strategies and measures according to the predicted fault risk to reduce the probability of future faults, and the suggested maintenance scheme comprising periodic inspection, part replacement and equipment maintenance is provided in the diagnosis result to reduce the probability of future faults.
Firstly, modeling an actual power supply system, and mapping the actual power supply system into a virtual environment by using simulation software, wherein the modeling comprises simulating the physical structure, electrical elements, characteristics and behaviors of a control system and sensors of the power supply device; in the virtual power supply system, various faults and abnormal conditions are simulated, including voltage fluctuation, current overload and wire short circuit, and various possible fault conditions are simulated in the virtual environment by adjusting simulation parameters and setting fault scenes; the virtual power supply system can collect various key performance indexes and sensor data in real time, wherein the data comprise voltage, current and power, and the data can be transmitted to a fault diagnosis model through a network; inputting real-time data acquired by a virtual power supply system into a model for analysis and diagnosis by applying an established fault diagnosis model, judging whether a fault occurs or not by the model according to a data mode and characteristics based on machine learning and deep learning and technologies, and positioning the cause and the position of the fault; once the fault is diagnosed, the virtual power system simulation environment provides real-time repair guidance, generates recommended lists of repair steps, repair tools and required spare parts for specific fault types and positions, and the guidance is visually presented to provide visual operation guidance and repair flow.
According to the invention, an automatic algorithm and an artificial intelligence technology are introduced to perform automatic fault diagnosis, so that faults are rapidly and accurately positioned, corresponding solutions are provided, and the time for fault maintenance is shortened; the power supply system is monitored and diagnosed in real time through the internet connection and the remote access technology, so that maintenance personnel can remotely perform fault elimination, the downtime and the cost are reduced, and fault information can be obtained in real time so as to be further analyzed and prevented; by developing an intelligent diagnosis tool and a user interface which are easy to use and operate, a friendly interface is provided for a user, the user is guided to carry out fault diagnosis, possible reasons are explained, and suggestions or steps are given to solve the problem; by collecting and analyzing information such as historical data, running states, environmental parameters and the like of the power supply system, potential fault risks are found in time, preventive measures are taken, and the occurrence rate of future faults is reduced.
Example two
As shown in fig. 1 to 2, a fault diagnosis method includes the steps of:
s1, installing and configuring a sensor and a measuring device, wherein the sensor and the measuring device are used for collecting power performance data, and preprocessing the collected data, including filtering, denoising and normalizing;
S2, extracting features from the preprocessed data, wherein the features comprise features of time domain, frequency domain and wavelet transformation, and selecting the features with the most information quantity for subsequent fault diagnosis according to the importance degree of the features;
s3, establishing a fault diagnosis model by using a machine learning, deep learning and expert system method, training the model by using marked training data, and evaluating and optimizing model parameters by using a verification set;
s4, configuring a remote communication unit, ensuring that power supply equipment, a cloud platform and a central monitoring system are in real-time communication, transmitting power supply performance data to the cloud platform and the central monitoring system, ensuring the real-time performance and reliability of the data, and providing a remote access interface and a control unit so that a user can remotely monitor and control the power supply system;
s5, integrating a sensor and a fault detection circuit on the power supply equipment, wherein the sensor and the fault detection circuit are used for monitoring the power supply state and detecting abnormal conditions in real time, configuring an alarm device, and sending out corresponding alarm signals when faults or abnormal conditions are detected;
s6, designing a user interface so that a user can input related information, providing an interface which is easy to operate and use, processing and analyzing data input by the user, executing a fault diagnosis algorithm to determine the cause and the position of the fault, visually displaying a diagnosis result to the user, and providing corresponding solutions and suggestions;
S7, collecting and storing historical data, running states and environmental parameter information of the power supply system, analyzing and modeling the historical data by using a data analysis and modeling technology, establishing a power supply fault prediction model, and providing corresponding prevention strategies and measures according to predicted fault risks so as to reduce the probability of future faults;
s8, automatically triggering a work order system of a maintainer according to a prediction result of the fault diagnosis model so as to timely perform fault repair and maintenance work;
s9, transmitting the fault diagnosis model and real-time data to the mobile terminal equipment by using a wireless communication technology, so that maintenance personnel can perform fault diagnosis and repair work at any time and any place;
s10, setting a virtual power system simulation environment, and providing real-time fault diagnosis and repair guidance;
s11, integrating the fault diagnosis model and the prediction model with a supply chain management system to realize automatic spare part allocation and fault repair processes, and improving the operation efficiency of the power supply system and reducing the maintenance cost.
The fault diagnosis model in the step S3, wherein training data of the fault diagnosis model comprise power failure reasons, and the power failure reasons stored in the fault diagnosis model comprise overload, short circuit, overvoltage, undervoltage, fault protection, temperature problems, power element faults, environmental interference and misoperation;
Overload refers to the fact that the power supply load exceeds the rated capacity of the power supply, so that the power supply cannot provide enough electric energy, and the reasons for overload include that the equipment is connected with too many loads, a certain load suddenly increases, voltage drop and current increase of the power supply are caused, and even a power fuse trips; short circuit refers to low impedance connection between the output ends of the power supply or between the output ends of the power supply and the ground, so that excessive current is caused, the short circuit causes damage to wires, connectors and electric elements, and the short circuit causes tripping of a power fuse and starting of a protection mechanism for overload protection of the power supply; overvoltage means that the voltage output by a power supply exceeds the rated value of the power supply, and the reasons include voltage fluctuation of a power grid and failure of the power supply, and the connected equipment is damaged by the excessive voltage, so that the equipment fails; the undervoltage refers to that the voltage output by the power supply is lower than the rated value of the power supply, and the reasons include lower voltage in a power grid, failure of internal components of the power supply, failure of the equipment caused by the undervoltage and unstable power supply; the fault protection means that when a protection mechanism in the power supply detects internal faults and external anomalies of the power supply, the protection mechanism comprising overcurrent protection and overtemperature protection is triggered, and when the protection mechanism is triggered, the power supply stops outputting electric energy so as to prevent further damage and dangerous situations; the temperature problem means that the high temperature environment can cause ageing and overheating of the power supply assembly, so that the efficiency and reliability of the power supply are reduced, and the performance of the power supply is also affected in the low temperature environment; the power supply element fault means that the electronic elements in the power supply, including a capacitor, a transformer and a relay, also can fail, so that the power supply works abnormally; environmental interference refers to the fact that the power supply is affected by environmental interference from other electromagnetic equipment, radioactive substances, electromagnetic waves, electromagnetic pulses and lightning strokes, which can cause interference and damage to the power supply, resulting in power failure; incorrect operation means that incorrect use and operation of the power supply device can lead to malfunctions, including incorrect wiring and incorrect setting of parameters.
The specific method for judging the power failure in the S5 comprises the following steps:
monitoring the flicker and color change of the power indicator and the display of error codes of the display screen, and primarily presuming and judging the fault type; the voltage meter and the ammeter are connected to the output end of the power supply and the key circuit, the values of the voltage and the current are monitored in real time, and abnormal voltage and current values represent the fault types of overload, short circuit, overvoltage and undervoltage; the fuse and the fuse are common protection devices in the power supply, damage can occur when faults occur, and whether overload and short-circuit faults occur is primarily judged by guiding a user to check whether the fuse and the fuse are blown and burned; detecting the temperature conditions in and around the power supply equipment by monitoring the data of the infrared thermometer and the sensor, wherein the temperature which is obviously higher than or lower than the normal working temperature range represents the temperature problem and overload fault; abnormal noise and vibration indicate the failure of the internal components of the power supply, including the damaged capacitor and fan, and the failure type is primarily determined by analyzing the data of the vibration sensor.
The solutions and suggestions provided for the user in S6 include:
when the fault type is overload and short circuit, firstly checking the load condition, confirming whether excessive equipment is connected to a power supply, if so, reducing the load and redistributing the load, then checking whether the wires among the power line, the socket and the equipment are loose and damaged, repairing and replacing damaged parts, and finally, if the fuses and the fuses are found to burn out, replacing new fuses and fuses according to the specification;
When the fault type is overvoltage and undervoltage, firstly checking whether the input voltage of the power supply is normal or not, if so, contacting the power supplier for processing, and secondly, for the condition of frequently generating overvoltage and undervoltage, maintaining stable power supply output by using a voltage stabilizer and adjusting constant output parameters of the power supply;
when the fault type is fault protection, firstly, according to a power manual and a user guide, executing corresponding steps to reset a protection mechanism, then checking environmental factors to ensure that the surrounding environment is suitable for normal operation of a power supply, including proper temperature and good ventilation, and finally checking whether internal elements of the power supply are damaged and aged or not, and repairing and replacing the fault elements according to requirements;
when the fault type is a temperature problem, firstly ensuring good heat dissipation, removing plugs, cleaning fans, ensuring enough space to be reserved around power supply equipment for ventilation, secondly strengthening heat dissipation means, and adding heat dissipation devices comprising fans and radiators;
when the fault type is environmental interference, firstly, an interference source is found and the propagation of an interference signal is isolated as much as possible, and secondly, a filter and a suppressor are arranged to reduce the influence of the interference signal;
when the fault type is incorrect operation, the user manual is first carefully read, the user manual, the related art support file is consulted, the correct operation and setting of the power supply device are ensured, and then the related parameters of the power supply device are reconfigured according to the user manual and related instructions, so that the correct operation and use are ensured.
According to the invention, an automatic algorithm and an artificial intelligence technology are introduced to perform automatic fault diagnosis, so that faults are rapidly and accurately positioned, corresponding solutions are provided, and the time for fault maintenance is shortened; the power supply system is monitored and diagnosed in real time through the internet connection and the remote access technology, so that maintenance personnel can remotely perform fault elimination, the downtime and the cost are reduced, and fault information can be obtained in real time so as to be further analyzed and prevented; by developing an intelligent diagnosis tool and a user interface which are easy to use and operate, a friendly interface is provided for a user, the user is guided to carry out fault diagnosis, possible reasons are explained, and suggestions or steps are given to solve the problem; by collecting and analyzing information such as historical data, running states, environmental parameters and the like of the power supply system, potential fault risks are found in time, preventive measures are taken, and the occurrence rate of future faults is reduced.
Claims (1)
1. A fault diagnosis method, characterized in that: the method comprises the following steps:
s1, installing and configuring a sensor and a measuring device, wherein the sensor and the measuring device are used for collecting power performance data, and preprocessing the collected data, including filtering, denoising and normalizing;
S2, extracting features from the preprocessed data, wherein the features comprise features of time domain, frequency domain and wavelet transformation, and selecting the features with the most information quantity for subsequent fault diagnosis according to the importance degree of the features;
s3, a fault diagnosis model is established by using a machine learning and deep learning method, the model is trained by using marked training data, and model parameters are evaluated and optimized by using a verification set;
s4, configuring a remote communication unit, ensuring that power supply equipment, a cloud platform and a central monitoring system are in real-time communication, transmitting power supply performance data to the cloud platform and the central monitoring system, ensuring the real-time performance and reliability of the data, and providing a remote access interface and a control unit so that a user can remotely monitor and control the power supply system;
s5, integrating a sensor and a fault detection circuit on the power supply equipment, wherein the sensor and the fault detection circuit are used for monitoring the power supply state and detecting abnormal conditions in real time, configuring an alarm device, and sending out corresponding alarm signals when faults or abnormal conditions are detected;
s6, designing a user interface so that a user can input related information, providing an interface which is easy to operate and use, processing and analyzing data input by the user, executing a fault diagnosis algorithm to determine the cause and the position of the fault, visually displaying a diagnosis result to the user, and providing corresponding solutions and suggestions;
S7, collecting and storing historical data, running states and environmental parameter information of the power supply system, analyzing and modeling the historical data by using a data analysis and modeling technology, establishing a power supply fault prediction model, and providing corresponding prevention strategies and measures according to predicted fault risks so as to reduce the probability of future faults;
s8, automatically triggering a work order system of a maintainer according to a prediction result of the fault diagnosis model so as to timely perform fault repair and maintenance work;
s9, transmitting the fault diagnosis model and real-time data to the mobile terminal equipment by using a wireless communication technology, so that maintenance personnel can perform fault diagnosis and repair work at any time and any place;
s10, setting a virtual power system simulation environment, and providing real-time fault diagnosis and repair guidance;
s11, integrating a fault diagnosis model and a prediction model with a supply chain management system to realize automatic spare part allocation and fault repair processes, and improving the running efficiency of a power supply system and reducing the maintenance cost;
the system comprises an automatic fault diagnosis module, a data acquisition unit, a data preprocessing unit, a feature extraction unit, a fault diagnosis algorithm unit and a result display and report unit, wherein the S1, the S2 and the S3 are supported by the automatic fault diagnosis module;
The data acquisition unit is responsible for collecting power performance data, including voltage, current and power parameters, and the voltage sensor uses the following formula to measure: u=r×i, where U is voltage, R is resistance, and I is current; the data preprocessing unit performs preprocessing operations of filtering, denoising and normalizing on the acquired data so as to facilitate subsequent analysis, the filtering eliminates high-frequency noise and low-frequency noise through a digital filter, the denoising eliminates abnormal values through a signal processing algorithm comprising wavelet transformation and mean value filtering, and the normalization is used for mapping data in different ranges to a unified numerical range; the feature extraction unit extracts useful features from the preprocessed data for input of a fault diagnosis algorithm, and extracts frequency domain features by calculating time domain statistical features of means, variances, peaks and valleys of the signals and converting the signals from the time domain to the frequency domain using a fast fourier transform; the fault diagnosis algorithm unit uses machine learning and deep learning algorithms to judge the fault type and position according to the extracted characteristics, establishes a fault diagnosis model by using a support vector machine, a random forest and a convolutional neural network, and classifies the fault diagnosis model by inputting the characteristics; the result display and report unit displays the diagnosis result to maintenance personnel, including using the form of charts and reports to present, and generating a diagnosis report and a proposal solution;
The S4 is supported by a remote monitoring and processing module, and the remote monitoring and processing module comprises a remote communication unit, a data transmission unit, a remote access interface unit and a remote control unit;
the remote communication unit realizes communication between the power supply system and the remote monitoring equipment through Ethernet, wireless network and Bluetooth connection, and in the Ethernet, the communication between the equipment is realized by using a standard TCP/IP protocol; the data transmission unit transmits the power performance data to the cloud platform and the central monitoring system, so that the effectiveness of real-time monitoring and diagnosis is ensured, and the data is transmitted by using a HTTP, MQTT, webSocket protocol, so that the real-time performance and reliability of the data are ensured; the remote access interface unit provides an interface for a user to remotely access the power supply system and acquire key parameters and fault information, and the user logs in by using a user name and a password and then checks the key parameters and the fault information; the remote control unit allows maintenance personnel to remotely control the power supply system to perform fault removal and repair operations, and controls equipment by using command transmission protocols including SSH and Telnet;
the S5 is supported by a hardware integrated diagnosis functional module, and the hardware integrated diagnosis functional module comprises a sensor unit, a fault detection circuit unit and an alarm device unit;
The sensor unit integrates related sensors into power equipment, and comprises a voltage sensor, a current sensor and a temperature sensor, wherein key parameters of voltage, current and temperature are monitored; the fault detection circuit unit monitors the power supply state and detects abnormal conditions through a designed fault detection circuit, and the overload protection circuit judges whether overload occurs or not through monitoring whether the current value exceeds the rated range or not; when the alarm device unit is in fault or abnormal condition, the alarm device unit sends out alarm signals of sound and light through the alarm device, and the alarm device unit comprises a buzzer, an LED indicator lamp and an alarm triggering notification;
the S6 is supported by an intelligent diagnosis tool and a software application module, wherein the intelligent diagnosis tool and the software application module comprise a user interface display unit, a data input and processing unit, a fault diagnosis algorithm unit and a result display and suggestion unit;
the user interface display unit provides an interface which is easy to use and operate, and comprises a graphical user interface and a command line interface, wherein a user inputs related information comprising equipment names and fault descriptions through the interface; the data input and processing unit guides a user to input related information, processes and analyzes the input data so as to facilitate subsequent diagnosis operation, and converts fault description input by the user into structurable data by using a text analysis technology so as to facilitate the subsequent diagnosis operation; the fault diagnosis algorithm unit executes a fault diagnosis algorithm to determine the cause and the position of the fault according to the data input by the user and the existing knowledge base, and performs fault diagnosis by using an expert system based on a rule reasoning engine and a pattern matching algorithm; the result display and suggestion unit visually presents the diagnosis result to the user and gives out corresponding suggestions and solutions;
The S7 is supported by an integrated fault prediction functional module, and the integrated fault prediction functional module comprises a data collection and storage unit, a data analysis and modeling unit, a fault prediction algorithm unit and a prevention strategy and measure execution unit;
the data collection and storage unit collects and stores information of historical data, running states and environmental parameters of the power supply system, and the data storage is carried out by using a database and a cloud platform, so that reliability and expandability are ensured; the data analysis and modeling unit analyzes and models historical data by using the technology of data mining and machine learning to predict potential fault risks, and establishes a power failure prediction model by using a time sequence analysis method comprising an autoregressive moving average model and a long-term and short-term memory network; the fault prediction algorithm unit applies a statistical model, a time sequence analysis and a regression algorithm, performs fault prediction based on a modeling result, and realizes a prediction algorithm by using techniques of supervised learning, unsupervised learning and reinforcement learning; the prevention strategy and measure unit gives corresponding prevention strategies and measures according to the predicted fault risk so as to reduce the probability of future faults, and the diagnosis result is provided with a recommended maintenance scheme comprising periodic inspection, part replacement and equipment maintenance so as to reduce the probability of future faults;
The automatic fault diagnosis module, the remote monitoring and processing module, the hardware integrated diagnosis function module, the intelligent diagnosis tool and the software application module can detect power source fault reasons including overload, short circuit, overvoltage, undervoltage, fault protection, temperature problems, power source element faults, environmental interference and error operation;
overload refers to the fact that the power supply load exceeds the rated capacity of the power supply, so that the power supply cannot provide enough electric energy, and the reasons for overload include that the equipment is connected with too many loads, a certain load suddenly increases, voltage drop and current increase of the power supply are caused, and even a power fuse trips; short circuit refers to low impedance connection between the output ends of the power supply or between the output ends of the power supply and the ground, so that excessive current is caused, the short circuit causes damage to wires, connectors and electric elements, and the short circuit causes tripping of a power fuse and starting of a protection mechanism for overload protection of the power supply; overvoltage means that the voltage output by a power supply exceeds the rated value of the power supply, and the reasons include voltage fluctuation of a power grid and failure of the power supply, and the connected equipment is damaged by the excessive voltage, so that the equipment fails; the undervoltage refers to that the voltage output by the power supply is lower than the rated value of the power supply, and the reasons include lower voltage in a power grid, failure of internal components of the power supply, failure of the equipment caused by the undervoltage and unstable power supply; the fault protection means that when a protection mechanism in the power supply detects internal faults and external anomalies of the power supply, the protection mechanism comprising overcurrent protection and overtemperature protection is triggered, and when the protection mechanism is triggered, the power supply stops outputting electric energy so as to prevent further damage and dangerous situations; the temperature problem means that the high temperature environment can cause ageing and overheating of the power supply assembly, so that the efficiency and reliability of the power supply are reduced, and the performance of the power supply is also affected in the low temperature environment; the power supply element fault means that the electronic elements in the power supply, including a capacitor, a transformer and a relay, also can fail, so that the power supply works abnormally; environmental interference refers to the fact that the power supply is affected by environmental interference from other electromagnetic equipment, radioactive substances, electromagnetic waves, electromagnetic pulses and lightning strokes, which can cause interference and damage to the power supply, resulting in power failure; incorrect operation means that incorrect use and operation of the power supply device may cause malfunctions, including incorrect wiring and incorrect setting parameters;
The specific method for judging the power failure by the automatic failure diagnosis module, the remote monitoring and processing module, the hardware integrated diagnosis function module, the intelligent diagnosis tool and the software application module comprises the following steps:
monitoring the flicker and color change of the power indicator and the display of error codes of the display screen, and primarily presuming and judging the fault type;
the voltage meter and the ammeter are connected to the output end of the power supply and the key circuit, the values of the voltage and the current are monitored in real time, and abnormal voltage and current values represent the fault types of overload, short circuit, overvoltage and undervoltage;
the fuse and the fuse are common protection devices in the power supply, damage can occur when faults occur, and whether overload and short-circuit faults occur is primarily judged by guiding a user to check whether the fuse and the fuse are blown and burned;
detecting the temperature conditions in and around the power supply equipment by monitoring the data of the infrared thermometer and the sensor;
abnormal noise and vibration indicate that the internal components of the power supply have faults, including damaged capacitors and fans, and the fault type is primarily judged by analyzing the data of the vibration sensor;
the solution provided for the user after the automatic fault diagnosis module, the remote monitoring and processing module, the hardware integrated diagnosis function module, the intelligent diagnosis tool and the software application module judge the power failure type comprises the following steps:
When the fault type is overload and short circuit, firstly checking the load condition, confirming whether excessive equipment is connected to a power supply, if so, reducing the load and redistributing the load, then checking whether the wires among the power line, the socket and the equipment are loose and damaged, repairing and replacing damaged parts, and finally, if the fuses and the fuses are found to burn out, replacing new fuses and fuses according to the specification;
when the fault type is overvoltage and undervoltage, firstly checking whether the input voltage of the power supply is normal or not, if so, contacting the power supplier for processing, and secondly, for the condition of frequently generating overvoltage and undervoltage, maintaining stable power supply output by using a voltage stabilizer and adjusting constant output parameters of the power supply;
when the fault type is fault protection, firstly, according to a power manual and a user guide, executing corresponding steps to reset a protection mechanism, then checking environmental factors to ensure that the surrounding environment is suitable for normal operation of a power supply, including proper temperature and good ventilation, and finally checking whether internal elements of the power supply are damaged and aged or not, and repairing and replacing the fault elements according to requirements;
when the fault type is a temperature problem, firstly ensuring good heat dissipation, removing plugs, cleaning fans, ensuring enough space to be reserved around power supply equipment for ventilation, secondly strengthening heat dissipation means, and adding heat dissipation devices comprising fans and radiators;
When the fault type is environmental interference, firstly, an interference source is found and the propagation of an interference signal is isolated as much as possible, and secondly, a filter and a suppressor are arranged to reduce the influence of the interference signal;
when the fault type is wrong operation, firstly carefully reading a user manual, referring to the user manual, ensuring correct operation and setting of the power supply equipment, and then reconfiguring relevant parameters of the power supply equipment according to the user manual to ensure correct operation and use;
modeling an actual power supply system in the step S11, and mapping the actual power supply system into a virtual environment by using simulation software, wherein the modeling comprises simulating the physical structure, electrical elements, characteristics and behaviors of a control system and sensors of the power supply device; in the virtual power supply system, various faults and abnormal conditions are simulated, including voltage fluctuation, current overload and wire short circuit, and various possible fault conditions are simulated in the virtual environment by adjusting simulation parameters and setting fault scenes; the virtual power supply system can collect various key performance indexes and sensor data in real time, wherein the data comprise voltage, current and power, and the data can be transmitted to a fault diagnosis model through a network; inputting real-time data acquired by a virtual power supply system into a model for analysis and diagnosis by applying an established fault diagnosis model, judging whether a fault occurs or not by the model according to a data mode and characteristics based on machine learning and deep learning and technologies, and positioning the cause and the position of the fault; once the fault is diagnosed, the virtual power system simulation environment provides real-time repair guidance, generates recommended lists of repair steps, repair tools and required spare parts for specific fault types and positions, and the guidance is visually presented to provide visual operation guidance and repair flow.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311348016.7A CN117150414B (en) | 2023-10-17 | 2023-10-17 | Fault diagnosis method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311348016.7A CN117150414B (en) | 2023-10-17 | 2023-10-17 | Fault diagnosis method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117150414A CN117150414A (en) | 2023-12-01 |
| CN117150414B true CN117150414B (en) | 2024-04-12 |
Family
ID=88884456
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311348016.7A Active CN117150414B (en) | 2023-10-17 | 2023-10-17 | Fault diagnosis method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117150414B (en) |
Families Citing this family (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117763410B (en) * | 2023-12-07 | 2024-08-16 | 湖南华高成套设备有限公司 | EMC overcurrent and overvoltage protection and fault diagnosis system |
| CN117517877A (en) * | 2024-01-05 | 2024-02-06 | 江苏泽宇智能电力股份有限公司 | Distributed traveling wave online measurement fault positioning system |
| CN118016268A (en) * | 2024-02-02 | 2024-05-10 | 徐州海奥斯医疗科技有限公司 | Intelligent medical system based on artificial intelligence |
| CN117978975A (en) * | 2024-02-29 | 2024-05-03 | 北京安达致远科技有限公司 | Network data module for video camera |
| CN117932322B (en) * | 2024-03-22 | 2024-06-04 | 山东冠县鑫恒祥面业有限公司 | Flour equipment fault diagnosis method and system |
| CN118187612A (en) * | 2024-03-26 | 2024-06-14 | 广东恒梓煜科技有限公司 | Automatic opening and closing-based cave depot protective door control system and cave depot protective door |
| CN118137677B (en) * | 2024-05-06 | 2024-10-18 | 中建安装集团有限公司 | Temporary electricity safety and energy consumption management and control system and method |
| CN118228187A (en) * | 2024-05-27 | 2024-06-21 | 北京国电光宇机电设备有限公司 | New energy automobile power supply system fault diagnosis method, system, equipment and medium |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105353651A (en) * | 2015-12-23 | 2016-02-24 | 中车大连机车车辆有限公司 | Microcomputer screen-combining simulation debugging method for locomotive master control system and electric pneumatic breaking system |
| CN111307453A (en) * | 2020-03-20 | 2020-06-19 | 朗斯顿科技(北京)有限公司 | Transmission system fault diagnosis method based on multi-information fusion |
| KR102264773B1 (en) * | 2020-11-30 | 2021-06-15 | 주식회사 국제기술인증원 | Fault Diagnosis System for Diagnosis and Control of Temperature, Partial Discharge and Noise of Transformers Using Sensors |
| KR102315669B1 (en) * | 2020-08-13 | 2021-10-20 | 전남대학교산학협력단 | Fault diagnosis system based on virtual sensor to improve energy efficiency of building heating and cooling facilities and Fault diagnosis method using the same |
| CN114138532A (en) * | 2021-12-03 | 2022-03-04 | 青岛慧志信息科技有限公司 | Remote fault diagnosis system based on project scheme simulation software architecture design |
| CN116070523A (en) * | 2023-02-21 | 2023-05-05 | 北京创拓国际标准技术研究院有限责任公司 | Simulation construction method for substation defect detection image sample |
| CN116717437A (en) * | 2023-07-19 | 2023-09-08 | 西安热工研究院有限公司 | Wind turbine generator system fault monitoring method and system |
| CN116862442A (en) * | 2023-07-21 | 2023-10-10 | 河南盘古科技发展有限公司 | Intelligent equipment management method and system based on big data analysis |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US11349306B2 (en) * | 2019-06-19 | 2022-05-31 | King Fahd University Of Petroleum And Minerals | Distribution grid fault analysis under load and renewable energy uncertainties |
-
2023
- 2023-10-17 CN CN202311348016.7A patent/CN117150414B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105353651A (en) * | 2015-12-23 | 2016-02-24 | 中车大连机车车辆有限公司 | Microcomputer screen-combining simulation debugging method for locomotive master control system and electric pneumatic breaking system |
| CN111307453A (en) * | 2020-03-20 | 2020-06-19 | 朗斯顿科技(北京)有限公司 | Transmission system fault diagnosis method based on multi-information fusion |
| KR102315669B1 (en) * | 2020-08-13 | 2021-10-20 | 전남대학교산학협력단 | Fault diagnosis system based on virtual sensor to improve energy efficiency of building heating and cooling facilities and Fault diagnosis method using the same |
| KR102264773B1 (en) * | 2020-11-30 | 2021-06-15 | 주식회사 국제기술인증원 | Fault Diagnosis System for Diagnosis and Control of Temperature, Partial Discharge and Noise of Transformers Using Sensors |
| CN114138532A (en) * | 2021-12-03 | 2022-03-04 | 青岛慧志信息科技有限公司 | Remote fault diagnosis system based on project scheme simulation software architecture design |
| CN116070523A (en) * | 2023-02-21 | 2023-05-05 | 北京创拓国际标准技术研究院有限责任公司 | Simulation construction method for substation defect detection image sample |
| CN116717437A (en) * | 2023-07-19 | 2023-09-08 | 西安热工研究院有限公司 | Wind turbine generator system fault monitoring method and system |
| CN116862442A (en) * | 2023-07-21 | 2023-10-10 | 河南盘古科技发展有限公司 | Intelligent equipment management method and system based on big data analysis |
Non-Patent Citations (2)
| Title |
|---|
| 变电设备检修仿真系统的应用分析;黄先海;《工业技术》;20110810;第39卷(第6期);第131页 * |
| 基于仿真数据的神经网络故障诊断方法;赵导;齐晓慧;田庆民;任红伟;甄云卉;;计算机工程与设计;20100516(09);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117150414A (en) | 2023-12-01 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN117150414B (en) | Fault diagnosis method | |
| US7822578B2 (en) | Systems and methods for predicting maintenance of intelligent electronic devices | |
| CN110941918B (en) | Intelligent substation fault analysis system | |
| CN102866313B (en) | Power tunnel cable running status comprehensive monitoring and controlling method | |
| CN107203816B (en) | Fault maintenance method and system for secondary equipment of power system | |
| KR102604708B1 (en) | Switchboard diagnosis system based on artificial intelligence and switchboard diagnosis method based on artificial intelligence | |
| JP2018014103A (en) | Portable field maintenance tool configured for multiple process control communication protocols | |
| JP2022058695A (en) | Portable field maintenance tool configured for multiple process control communication protocols | |
| CN118112359B (en) | Switch cabinet fault diagnosis method and system based on Internet of things | |
| CN113763667A (en) | Fire early warning and state monitoring device and method based on 5G edge calculation | |
| CN117406026A (en) | Power distribution network fault detection method suitable for distributed power supply | |
| CN116707144B (en) | Low-voltage distribution box fault early warning method | |
| KR20210012200A (en) | Maintenance system for environment test apparatus using machine self check sensor and the control method thereof | |
| CN117639251A (en) | Intelligent online monitoring system for high-voltage switch cabinet | |
| CN117533180A (en) | Evaluation method and system for safety protection of charging pile | |
| CN117791876A (en) | Substation equipment operation state monitoring method and abnormal control system | |
| CN117406048B (en) | Transformer discharge fault diagnosis method and device | |
| CN117728571A (en) | Power distribution network line transformer safety protection system and method | |
| CN111638416A (en) | Fault monitoring device and method for power distribution cabinet of power distribution room | |
| CN112068006A (en) | Laboratory equipment safe operation and maintenance platform based on cloud computing | |
| CN116094582A (en) | Optical cable optical power abnormity monitoring system based on RPA | |
| CN112255554A (en) | Direct current system battery health status and internal fault monitoring device | |
| CN111342552A (en) | Early warning system for electric power operation maintenance and preventive test of 0.4KV power distribution system | |
| CN114116395B (en) | Aging room power supply line power overload protection method, system and device | |
| CN118609339A (en) | Fire safety management system for electric appliance circuit |
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 |