36 results about "Fault detection algorithm" patented technology

The invention relates to the technical field of operation and maintenance of data centers, and discloses an intelligent operation and maintenance monitoring method and system of a data center, and the method comprises the steps: building a hierarchical monitoring frame, carrying out the hierarchical data collection and standardization processing of three layers, namely, physical equipment, virtual resources and application services, and constructing an interlayer data sharing model; performing anomaly scanning on the running state of each layer by using a preset fault detection algorithm, triggering an alarm once an anomaly is found, and determining a propagation path influenced by a fault through data backtracking; and then analyzing a causal relationship between anomaly and cross-layer data by using an association rule mining algorithm, performing multi-dimensional verification in combination with a decision support rule base, and finally generating a fault positioning conclusion and a repair priority. In addition, response scripts can be automatically called for parameter adjustment, rapid positioning and automatic repair of faults are achieved, and the operation and maintenance efficiency and reliability of the data center are effectively improved.

The invention provides a dynamic fault-tolerant control method based on an expected function safety mechanism, and the method comprises the steps: 1, selecting the model of a sensor, and enabling the sensor to be distributed at the periphery of a vehicle body; 2, building a decision algorithm model, carrying out environment scene recognition, planning a driving path and speed according to data obtained by a sensing layer, evaluating safety risks of different decisions, and carrying out judgment and decision making on various conditions; 3, control interfaces of a vehicle power system, a steering system and a braking system are established, and the electronic control unit converts a decision control instruction output by the decision algorithm model into actual vehicle operation through the control interfaces; 4, performing an expected function safety mechanism, performing real-time monitoring on state parameters of the vehicle, and analyzing detection data through a fault detection algorithm; 5, implementing a fault tolerance strategy; and 6, continuously monitoring the actual driving state and the system response condition of the vehicle, and adjusting the fault tolerance strategy.

The invention discloses a nonlinear system robust residual error multi-fault detection and reconstruction method based on a sliding-mode observer, and solves the problems that an actuator fault and unknown input disturbance in a servo driving system are closely related and mutually coupled, and a fault detection algorithm is insensitive to identification of a first-degree fault, so that fault report failure or misjudgment is caused. According to the method, a general nonlinear system is transformed into two subsystems with independent fault influence conditions based on global differential homeomorphic transformation for the first time. Equivalently transforming reverse channel disturbance to forward channel disturbance by using a filter, designing a sliding mode state observer according to different actuator fault types, and respectively extracting fault information and unknown input disturbance information of a servo driving system in the two subsystems, thereby realizing decoupling of the fault and the unknown input disturbance. And the sensitivity of fault signal detection and the accuracy and robustness of fault diagnosis are effectively improved.

The invention discloses a gray fault processing method and device, a medium and a program product, and relates to the technical field of detection, and the method comprises the steps: collecting the performance data of all server nodes; according to the performance data of all the server nodes and the graph neural network model, detecting the server nodes with gray faults in the server cluster; in response to the grey fault of any server node in the server cluster, verifying the grey fault of the first server node through a single-node grey fault detection algorithm; determining a target migration server node through a server node health scoring mechanism and a topology model of a network link in response to the fact that the gray fault verification of the first server node is correct; and migrating the calculation task of the first server node to the target migration server node. According to the method, the gray fault in the server cluster is detected, the gray fault detection speed and accuracy are remarkably improved, the requirement of manual detection is reduced, and the reliability of the whole system is improved.

The application discloses a kind of underground pipeline detection method and system based on multiple information sources, which comprises the following steps: pipeline detection system obtains the historical information of regional pipeline, arranges pipeline detection sensor;Establish detection sub-region, obtain pipeline characteristic parameter;Design regional fault detection algorithm, obtain health center point, calculate regional fault coefficient, determine fault sub-region;Pipe robot is put into fault sub-region, design adaptive fault point detection algorithm, detect fault point, record fault point position;Output pipeline detection result, notify pipeline inspection personnel;The scheme realizes the fault detection of underground pipeline by using multiple information sources, improves the detection efficiency by using the historical information of pipeline and establishing the arrangement scheme of pipeline detection sensor, divides the entire pipeline by establishing sub-region, detects each sub-region separately, improves the detection efficiency and accuracy of pipeline fault, and reduces the safety risk caused by pipeline fault.

This invention proposes a multi-stage braking elevator safety clamp control method and system, belonging to the field of elevator braking technology. It includes a base mounted on the elevator car; an elevator guide rail mounted inside the elevator shaft and extending vertically; two sets of synchronous servo electric telescopic mechanisms, positioned opposite each other and spaced apart on the base, located on opposite sides of the elevator guide rail's extension direction; each set of synchronous servo electric telescopic mechanisms has at least two safety clamps at its output end; an acceleration sensor for acquiring the elevator car's acceleration signal; and an intelligent control unit communicatively connected to the acceleration sensor and the two sets of synchronous servo electric telescopic mechanisms. Based on a dual-branch fault detection algorithm using built-in dynamic convolutional branches and improved LSTM branches, when a fault is detected in the elevator, the control unit activates the two sets of synchronous servo electric telescopic mechanisms to perform staged braking of the elevator car when the elevator's acceleration exceeds a set threshold.

The invention discloses a servo motor driver parameter management method and system, and relates to the technical field of motor parameter management, and the method comprises the steps: initializing a parameter database, and outputting a database initialization result; executing a state machine control flow based on a database initialization result; according to the state information of the state machine, executing a corresponding parameter synchronization strategy and outputting a parameter synchronization state; based on the parameter synchronization state and the state machine state information, performing hierarchical error detection and processing, identifying abnormity through a fault detection algorithm based on a time window, performing fault processing according to a hierarchical recovery strategy, and outputting a system health state; and executing system diagnosis and performance optimization according to the system health state and the parameter synchronization state, performing read-write optimization through the parameter cache layer, and implementing predictive maintenance to complete parameter management. Through unified storage and management of data parameters, the reliability of parameter management of the servo motor is improved, and the system has the advantages of high fault identification precision and remarkably improved system stability.

The invention relates to an AGC hydraulic system fault detection method and computer equipment thereof. Data processing modes of a feature extraction method are different (model selection of a preprocessing step and a data processing step). Firstly, candidate measurements needing to be further researched are selected by using typical fault characteristic parameter analysis; second, the selected measurements are analyzed using a feature extraction method. The step aims to extract hidden features so as to detect internal faults to the greatest extent; and finally, the classification efficiency of the fault detection algorithm is evaluated by studying the detection rate of the proposed architecture. According to the method, the detection rate of the accuracy of the AGC hydraulic system is improved, and quick diagnosis and positioning of common faults of the AGC hydraulic system of the automatic rolling mill are realized to a certain extent.

The invention discloses a wind power plant primary frequency modulation method and an energy management platform, and relates to the technical field of wind power energy. The wind power plant primary frequency modulation method comprises a step following mode and a random disturbance mode. And when the step following mode is lowered, the active power set value of each wind turbine generator is adjusted based on a three-dimensional self-optimization algorithm, and accurate power regulation and control are realized. And during up-regulation, a falling prevention and potential fault sensing algorithm is adopted to pre-determine large-amplitude power falling or potential faults caused by energy over-support caused by wind speed abrupt change of the wind turbine generator, and a strategy of reducing an active power set value of the corresponding wind turbine generator in advance or directly quitting primary frequency modulation is adopted to avoid large-amplitude power falling and off-grid risks. In a random disturbance mode, aiming at the problem that response is not timely due to frequent fluctuation of a control target value, active power of each wind turbine generator is flexibly adjusted based on an adaptive anti-disturbance algorithm, and large control errors and integral electric quantity assessment risks due to the fact that the response is not timely are reduced.

The invention provides an industrial process fault detection cloud system based on LoRa and PCA, and belongs to the technical field of industrial process fault detection. The technical problems that an existing wired system is complex in wiring and limited in communication distance, and a conventional wireless system is short in transmission distance, single in function, lack of fault detection and power supply guarantee and insufficient in simulation verification capacity are solved. The cloud system comprises a LoRa node plate embedded with a PCA (Principal Component Analysis) fault detection algorithm, a LoRa gateway plate, an industrial process simulation platform and a cloud platform, the industrial process simulation platform, the LoRa node plate and the LoRa gateway plate are sequentially connected through LoRa wireless communication, and the LoRa gateway plate is connected with the cloud platform through 4G communication. According to the invention, the dual functions of actual data acquisition and semi-physical simulation are realized, the PCA algorithm is embedded in the node plate, the industrial process is monitored, the fault can be quickly identified, and the fault detection effect is achieved.

The present application belongs to the technical field of fault identification, and particularly relates to a low-voltage series arc fault identification method based on multi-frequency electrical quantity amplitude-phase combination. The steps include: each downstream monitoring point real-time monitors the voltage waveform distortion rate of adjacent cycles, and judges whether it is greater than or equal to the set distortion rate threshold; if yes, the fault detection algorithm is started; the voltage data of the downstream monitoring point and the corresponding upstream monitoring point are aligned asynchronously and in phase, differentiated to obtain the differential voltage, low-pass filtered, and the voltage characteristic energy is calculated; the minimum point of the voltage characteristic energy is obtained, the voltage energy peak values in the left and right windows are obtained and the corresponding phases are calculated; the peak threshold and the fault sensitive domain are determined, and the counting accumulation is performed; if the counting accumulation exceeds the counting threshold, it is determined as a fault. The present application can improve the sensitivity, accuracy and anti-interference ability of the power grid side fault detection.

The invention relates to a distribution network short-circuit fault rapid detection algorithm based on voltage and current synchronous trigger detection. The method comprises the following steps: S1, designing a group of multi-scale low-pass filters, and limiting the highest frequency of voltage and current signals by using the low-pass filters; s2, filtering the filtered voltage and current signals by using a multi-scale low-pass filter, and calculating a filter output difference value between adjacent scales as an abrupt change detection quantity; s3, finding an extreme value of the mutation detection quantity, and calculating an average value of absolute values in specified window widths on the left and right sides of the extreme value; s4, if the short-circuit fault detection is started, starting from the sudden change position of the voltage and the current, using a Savitzky-Golay differentiator to calculate 0-order, 1-order and 2-order normalized differentiations of the voltage and the current in the window after the sudden change position; s5, if the short-circuit fault is detected at the short-circuit fault detection moment but not at the starting moment, judging whether the time difference between the sudden change points exceeds a threshold value or not; if so, detection of the short circuit fault is stopped.

The application relates to the field of fault detection, and provides a bearing fault detection algorithm and system. An original vibration signal is decomposed through a local mean decomposition algorithm, and an effective PF component is screened out by using a Pearson correlation coefficient, and then signal reconstruction is carried out, irrelevant noise signal interference is removed, feature extraction of a bearing degradation process is realized based on a product form of a harmonic noise ratio and a root mean square value, the sensitivity of the harmonic noise ratio to periodic impact is fully utilized, and the problem that the root mean square value has low sensitivity to initial bearing degradation is avoided; a state space model of different bearing degradation processes is constructed based on a Paris model and a Foreman model, compared with a single model prediction model, the prediction precision of RUL is more effectively improved; regularization of a particle filtering resampling process is realized based on an Euclidean distance, the diversity of particles is effectively improved, the particle depletion phenomenon of basic particle filtering is avoided, and the filtering estimation precision of a nonlinear state is improved.

One example includes a wind turbine yaw control fault detection system. The system includes current monitors that are each configured to monitor a current amplitude of a respective one of a plurality of yaw motors of a wind turbine and to generate a current signal that is indicative of the respective current amplitude. The system further includes a processor to compare the current amplitude of each of the yaw motors relative to each other and relative to at least one threshold based on the current signal from each of the current monitors. The fault detection algorithm further determines a fault condition associated with at least one yaw mechanical drive component of the wind turbine based on the comparison of the current amplitude of each of the yaw motors relative to each other and relative to at least one threshold.

The invention belongs to the technical field of fault identification, and particularly relates to a low-voltage series arc fault identification method based on multi-frequency electrical quantity amplitude-phase combination, which comprises the following steps: each downstream monitoring point monitors the voltage waveform distortion rate of adjacent cyclic waves in real time, and judges whether the voltage waveform distortion rate is greater than or equal to a set distortion rate threshold value; if yes, starting a fault detection algorithm; carrying out asynchronous-in-phase alignment and difference on the voltage data of the downstream monitoring point and the corresponding upstream monitoring point to obtain a differential voltage, carrying out low-pass filtering, and calculating voltage characteristic energy; obtaining a voltage characteristic energy minimum value point, obtaining voltage energy peak values in the left window and the right window, and calculating corresponding phases; judging a peak threshold value and a fault sensitive domain, and counting and accumulating; and if the counting accumulation exceeds the counting threshold value, a fault is determined. According to the invention, the sensitivity, accuracy and anti-interference capability of power grid side fault detection can be improved.

Fault detection system (112) for a drive system (110) with a selective catalytic reduction unit, SCR (206), comprising: a memory (54) configured to store a fault detection algorithm relating to nitrogen oxides, NOx, in the propulsion system (110); and a processor (50) which is operationally coupled to the memory (54) and programmed to: (a) to obtain a first NOx concentration value relating to an upstream input (203) of the SCR unit (206) and (b) a second NOx concentration value relating to a downstream output (205) of the SCR unit (206); (a) and (b) to be temporarily stored in response to receiving (a) and (b) and to repeat the retrieval and buffering until buffered N values ​​have been obtained, where N is a pre-programmed number; to calculate a front-end average of NOx and a front-end standard deviation using the cached N-values; to calculate a downstream average of NOx and a downstream standard deviation using the cached N values; to use the upstream average of NOx, the upstream standard deviation, the downstream average of NOx and the downstream standard deviation as input for the fault detection algorithm relating to nitrogen oxides, NOx, in the propulsion system (110) in order to generate a linear correlation factor; and A non-acceptable part with the best performance, BPU, is detected when the linear correlation factor is greater than a pre-programmed error threshold; where the error detection algorithm includes rules for generating a Pearson correlation; and where the processor (50) is further programmed to: In addition to (a) and (b), to obtain: (c) an upstream mass flow rate value, (d) an upstream temperature value, (e) a downstream mass flow rate value, (f) a downstream temperature value, and (g) a urea value; and Temporarily storing (a), (b), (c), (d), (e), (f) and (g) in response to obtaining (a), (b), (c), (d), (e), (f) and (g); and where each of the cached N values ​​comprises a respective (a), (b), (c), (d), (e), (f) and (g).

One example includes a wind turbine yaw control fault detection system. The system includes current monitors that are each configured to monitor a current amplitude of a respective one of a plurality of yaw motors of a wind turbine and to generate a current signal that is indicative of the respective current amplitude. The system further includes a processor to compare the current amplitude of each of the yaw motors relative to each other and relative to at least one threshold based on the current signal from each of the current monitors. The fault detection algorithm further determines a fault condition associated with at least one yaw mechanical drive component of the wind turbine based on the comparison of the current amplitude of each of the yaw motors relative to each other and relative to at least one threshold.

The application discloses a kind of terminal equipment's fault detection method, system and medium, the method includes the following steps: configuring fault detection program, fault detection equipment and fault detection algorithm;Setting detection reference value, first detection information and second detection information;Detection terminal equipment's start condition, based on fault detection program, fault detection algorithm, detection reference value, first detection information and start condition executes grounding detection step, obtains detection result;Based on fault detection program, fault detection equipment, fault detection algorithm, detection reference value, second detection information and detection result executes equipment operation detection operation or wiring abnormality detection operation;The application can intelligently detect the equipment grounding anomaly of terminal equipment under different operating conditions, and intelligently voice prompts the detection result, finally whether the fault cause of terminal equipment is caused by physical wiring error is judged intelligently, extremely convenient, improve the security of terminal equipment.

Embodiments of the present application propose a photovoltaic fault automatic detection method based on multispectral fusion. Hot spot and diode conduction fault are one of the most common faults in photovoltaic modules. Due to dust accumulation, tree shade and foreign matter obstruction, etc. can also cause uneven heat of photovoltaic panel. Directly using infrared image to identify photovoltaic fault has a large error. This brings great threat to the safety and economic operation of photovoltaic power station. This paper proposes a photovoltaic fault detection algorithm based on infrared-visible light fusion. The photovoltaic area of infrared and visible light images is segmented. Then considering the obvious corner features of the photovoltaic panel, the SIFT feature point extraction algorithm is used to register the infrared-visible light image. Then the infrared and visible light images are fused and the features are extracted by using the Transformer multi-head attention mechanism. Then the fused image after feature extraction and enhancement is sent into the deep learning network for defect recognition.

This application discloses a memory testing method, memory testing equipment, and computer program product. The method includes: executing a preset basic fault detection algorithm on multiple consecutive addresses within a memory region under test using at least one preset first test pattern array to detect basic faults in the multiple consecutive addresses; and executing a preset hold fault detection algorithm on the at least one first test pattern array and at least one preset second test pattern array to detect hold faults in the multiple consecutive addresses. Each second test pattern array is a binary sequence repeating every M bits, and each M bit includes at least one logical value with at least three consecutive transitions, where M is a natural number, and 8 ≤ M ≤ the number of bits in the memory cell. This application can significantly improve fault coverage and defect interception rate.

The invention discloses a detection algorithm for dynamic faults of a memory, and aims to realize 100% coverage of all non-associated static faults (including single-unit faults and coupling faults), non-associated dynamic single-unit faults (SCF) and Saa / Svv type double-unit dynamic faults (DCF). Based on the analysis of a system fault model, the designed March YZ algorithm is integrated with an optimized operation sequence, on the premise of reserving the fault activation, sensitization and detection capabilities, detection sequences of dTF, dWDF, dCFtr and dCFwd type dynamic faults are emphatically adjusted, and redundant read-write cycles are eliminated. The complexity of the March YZ algorithm is only 62n, is reduced by 8n compared with the existing March MD2 (70n), and is reduced by 4n compared with the March WY (66n); the test time is reduced by 11.4% and 6.1% respectively while full fault coverage is realized, the operation energy consumption of a memory is reduced by 5-15%, the area overhead of an MBIST circuit is reduced, and the method is adaptive to advanced process SoC (System on Chip) of 5nm and below, and can be applied to high-reliability scenes such as automotive electronics, aeronautics and astronautics, industrial control and the like.

The application discloses a gray fault processing method and device, medium and program product, relates to the technical field of detection, and comprises the following steps: collecting performance data of all server nodes; detecting server nodes with gray faults in a server cluster according to the performance data of all server nodes and a graph neural network model; in response to the occurrence of a gray fault in any one server node in the server cluster, verifying the gray fault of the first server node through a single-node gray fault detection algorithm; in response to the correct verification of the gray fault of the first server node, determining a target migration server node through a server node health scoring mechanism and a topology model of a network link; and migrating the computing task of the first server node to the target migration server node. The application significantly improves the gray fault detection speed and accuracy, reduces the need for manual detection, and improves the reliability of the overall system.

The invention relates to the technical field of feeder verification, in particular to an automatic feeder verification method, system and device based on a customizable net rack and a storage medium. A reconfigurable grid model is constructed by acquiring topological parameters of a power distribution network, a fault injection point is set based on the model, a fault area is identified through a fault detection algorithm, and a segmentation position of a fault boundary and a non-fault area is determined; generating a section switch coordination control sequence according to a fault boundary, evaluating a switch action time sequence to realize rapid isolation, constructing a power supply recovery path decision model in combination with an isolated net rack state, and optimizing a load transfer strategy to determine a recovery scheme; and analyzing correlation characteristics of system response time and recovery success rate based on a recovery scheme execution result to obtain a performance evaluation index, and dynamically adjusting a fault detection threshold and a control strategy in combination with historical verification data.

A multi-level overcurrent and grounding fault protection collaborative system includes a sensing and sampling module, a signal conditioning and feature extraction module, a fault diagnosis and protection execution module, a data statistics and communication module, and a security and access control module. The sensing and sampling module is used to collect power system data; the signal conditioning and feature extraction module is used to preprocess the raw data and extract time-frequency domain features; the fault diagnosis and protection execution module is used to perform fault detection and multi-level collaborative decision execution; the data statistics and communication module is used to build a fault event database and communicate with adjacent protection devices in real time; and the security and access control module is used to provide user permission management and system interaction control functions. This invention proposes a fault signal feature extraction algorithm based on machine learning to extract fault signal features and a deep learning-based overcurrent and grounding fault detection algorithm to detect and identify faults.

This invention discloses a testing method based on a fault detection platform for a main helium blower frequency converter, belonging to the field of industrial equipment fault detection technology. The method includes acquiring the original operating parameters of the target frequency converter, processing and generating a multi-dimensional time-series operating parameter stream; constructing a configurable fault mode library, separating core feature subsets and auxiliary feature subsets according to a key feature extraction strategy, and generating a hybrid feature sequence containing simulated fault information. The hybrid sequence is coupled with the auxiliary subset to construct a fault feature fusion matrix, which is input into an adaptability verification engine. The auxiliary subset simulates nuclear power environment constraints to verify the authenticity of the fault information, and outputs test cases with environment identification. This method uses iterative training of a fault detection algorithm to generate an optimized model adapted to the target environment and deploy it on the platform. It then performs online analysis on the new standardized multi-dimensional time-series operating parameter stream, outputting fault diagnosis results and confidence levels.

The application provides a wind turbine complete machine bolt monitoring system. The wind turbine complete machine bolt monitoring system comprises a sensor failure monitoring module, a plurality of high-precision flange bolt gap sensors installed at the fan blade flanges and the tower flange, which are used for monitoring the flange gap in real time, and a multi-channel flange bolt gap acquisition instrument, which is used for receiving the monitoring signals output by the flange bolt gap sensors and collecting data. The wind turbine complete machine bolt monitoring system effectively solves the problem of alarm omission caused by sensor failure or failure in the prior art by introducing a sensor failure monitoring module and a fault prediction module. The system uses a model-based fault detection algorithm to monitor the state of the sensor in real time, timely issues a warning signal when an abnormality occurs, thereby avoiding the blind spots of the traditional system and improving the operation safety and reliability of the wind turbine.

The invention relates to the field of automatic control of a power system, and discloses a power supply recovery method, device and equipment based on network construction energy storage and a storage medium, which are used for improving the efficiency and quality of power supply recovery. The power supply recovery method based on network construction energy storage comprises the following steps: acquiring operation state information of a power system in real time, positioning a fault and an affected area by using a fault detection algorithm when the fault occurs, and formulating a scheduling plan according to the fault and the affected area; globally optimizing the scheduling plan through a mixed integer linear programming algorithm to obtain a scheduling result containing power supply access point information and load point information of power supply to be recovered; and finding out a plurality of power supply paths by using a path search algorithm according to a scheduling result in combination with network topology. Screening through a multi-objective optimization algorithm to obtain an optimal recovery path; and finally, in combination with distributed energy coordination control and a micro-grid technology, executing power supply recovery according to an optimal path.

The invention relates to the technical field of power equipment operation and maintenance, and provides an embedded fault recording system which integrates a control device end, a monitoring equipment end, a power equipment end, a background control end and a user operation end, and constructs an electric energy early warning model to predict electric energy stability by monitoring electric energy information in a photovoltaic power station in real time. When the electric energy stability is abnormal, the system automatically carries out fault recording. The system further has the functions of data acquisition, processing, detection, model optimization and the like, accuracy and integrity of data acquisition are improved, abnormity is recognized and fault waveforms are recorded through a preset fault detection algorithm, and meanwhile, equipment configuration parameters are continuously optimized to improve accuracy and response speed of fault monitoring. In addition, the system further comprises an electric energy management module, a standby power switching module, an electric quantity monitoring module and the like, the power supply stability and reliability of the system are guaranteed, the fault monitoring capacity of the photovoltaic power station is effectively improved, and safe and stable operation of the power system is improved.

The invention discloses an integrated magnetic suspension energy storage flywheel dual-motor control method, and relates to the technical field of dual-motor energy storage control, and the method comprises the steps: collecting flywheel initial rotating speed, magnetic suspension current and motor state data, and generating flywheel initial state data; according to the flywheel initial state data, the flywheel rotating speed and the magnetic suspension state are adjusted through a fuzzy PID control algorithm, the flywheel rotating speed, the magnetic suspension current and the motor state data are monitored in real time, and flywheel stability control parameters are generated; and the motor power output is adjusted based on the power adjustment strategy, when it is detected that the power output is abnormal, a fault protection mode is automatically switched, the motor power and the flywheel rotating speed are automatically adjusted by adopting a fault detection algorithm, and a fault protection scheme is generated. The self-adaptive capacity, the energy efficiency and the safety of the flywheel system are improved, and efficient and stable operation under various working states is ensured.

The invention belongs to the technical field of photovoltaic systems, and particularly relates to a photovoltaic communication control system with a fault self-checking function, which comprises a photovoltaic assembly module, a data acquisition module, a communication module, a control module and an upper computer, and is characterized in that the data acquisition module comprises a voltage sensor, a current sensor, a temperature sensor and an illumination intensity sensor; key parameters such as voltage and current of the photovoltaic module are collected in real time through multiple types of sensors of the data collection module, faults such as overvoltage and temperature abnormity can be rapidly identified in combination with a fault detection algorithm and a data analysis model built in the control module, fault modules and types can be accurately positioned, the problems of lagging and fuzzy judgment of traditional manual inspection are solved, and the fault detection efficiency is improved. The photovoltaic assembly modules are in one-to-one correspondence with the sensors, so that the comprehensiveness of parameter acquisition is guaranteed; the communication module monitors the link state in real time, and automatically alarms and repairs when the state is abnormal; the control module has a self-diagnosis function, and the standby module can be switched when a fault occurs.