413 results about "Multiple fault" patented technology

The present invention relates to the field of fault diagnosis technology, specifically to a fault diagnosis method and system for a photovoltaic inverter, comprising the following steps: based on an operating status data set, analyzing a variety of inverter operating data, identifying the volatility of a variety of operating data, extracting statistical features and frequency domain features of the data, adjusting the detection threshold of abnormal data, and generating abnormal fluctuation detection parameters. In the present invention, the abnormal detection threshold is dynamically adjusted by analyzing the operating data, a more sensitive response is made to the data, the accuracy and response speed of the fault warning are improved, the time window segmentation and signal feature extraction are used to timely discover abnormal fluctuations, detect equipment damage or system failures, and the accuracy of fault type identification is improved by combining the comparison and analysis of data features. By analyzing the component types associated with various fault types, the efficiency and accuracy of locating faulty components are improved, and the reliability and economic benefits of the photovoltaic inverter are enhanced.

The invention discloses an optical cable fault positioning method based on an OTDR (Optical Time Domain Reflectometer), and relates to the technical field of optical cable fault positioning. Periodic waveform data of an optical cable is acquired through OTDR equipment, an echo spectrum is generated, a dynamic threshold algorithm is introduced according to the echo spectrum to carry out fault event identification, and a dynamic threshold value is generated; the processing end judges whether a fault point area exists in the echo based on the dynamic threshold value, if yes, the distance between the fault point and the OTDR equipment is calculated through propagation time delay, the spatial position of the fault point is determined, and after multiple fault points are recognized, fault types are classified according to reflection intensity. According to the positioning method, a dynamic threshold algorithm is introduced, so that the system has adaptive capacity to background environment change, the robustness of the system under long-distance, high-loss optical cable or severe noise conditions is remarkably enhanced, and weak reflection fault or hidden fault events neglected by a traditional method can be effectively identified.

Methods, computer program products, and systems are presented. The method computer program products, and systems can include, for instance: iteratively examining logging data; detecting multiple faults in a computer environment in dependence on the examining of the logging data; generating for respective ones of the detected multiple faults one or more candidate remediation to provide a set of candidate remediations for the computer environment; prioritizing remediations defining the set of candidate remediations from the generating and ordering the remediations in a remediation queue according to an order of the prioritizing; and deploying remediations according to the ordering of remediations in the remediation queue.

The invention provides a distribution network line dynamic optimization method based on multi-fault mode analysis, and relates to the technical field of distribution network management. Comprising the following steps: acquiring electrical parameters and real-time monitoring data of each fault section in a distribution network line, and acquiring a topological structure, a load demand and historical fault data of the distribution network line; carrying out load distribution optimization on the distribution network lines by adopting a dynamic load optimization algorithm according to the topological structure and the load demand of the distribution network lines in combination with the electrical parameters and the historical fault data, and adjusting the load distribution of each distribution network line based on optimization; and when a distribution network fault occurs, positioning a fault point and implementing a repair measure by combining the topological information of the distribution network and the electrical parameters of the fault section. According to the invention, by constructing the multi-fault mode analysis model, the fault occurrence position of the distribution network is detected in real time, and the fault point is quickly positioned; and in combination with an optimized repair strategy, the fault recovery time is effectively shortened, and the distribution network recovery efficiency is improved.

The invention discloses a method for evaluating the bearing time of an internal fault of large oil-filled main equipment, and belongs to the technical field of relay protection of oil-filled main equipment of a power system. Obtaining a plurality of fault currents in the internal fault current range of the oil-filled main equipment; establishing a three-dimensional geometric simulation model and a grid model of the oil charge main equipment; calling a transient pressure-based solver, simulating a turbulence phenomenon and an oil pressure rise phenomenon of the oil-filled main equipment under a fault current condition, judging whether the oil-filled main equipment is broken or not, and obtaining allowable fault bearing time of the oil-filled main equipment under different fault currents; and establishing a neural network model for mapping the fault current and the allowable fault withstanding time through a convolutional layer, a pooling layer and a full connection layer in the convolutional neural network, so as to evaluate the allowable fault withstanding time of the oil-filled main equipment. And the fracture and failure phenomena of the oil-filled main equipment in a high-stress state are accurately captured, so that a quantitative method which is more accurate than a traditional empirical method is provided for evaluating the fault bearing time of the oil-filled main equipment.

The invention discloses an automatic control method of a molded case circuit breaker operating mechanism, and particularly relates to the technical field of molded case circuit breakers. Electrical parameters are monitored in real time and transmitted to a control system, and comparative analysis is performed based on a preset threshold value and a fault model, so that faults are detected in time and a control instruction is generated, and the system can identify and sort multiple fault scenes, automatically execute switching-on or switching-off actions and ensure the reliability and stability of a power system; a judgment mechanism based on the circuit recovery condition is introduced, after the circuit is disconnected, whether recovery operation is carried out or not is reasonably judged according to the state of the circuit breaker and the recovery condition of the power system, the self-recovery capacity and the fault diagnosis capacity of the power system are effectively improved, the method can adapt to the influences of high loads, complex environments and power grid fluctuation, and the reliability of the power system is improved. And the reliability and the precision of an automatic control system are obviously improved.

The invention discloses an intelligent power grid fault prediction method, system and device, and relates to the technical field of power grid fault prediction, and the method comprises the steps: carrying out the real-time collection of the multi-source operation data of a target intelligent power grid, and obtaining a multi-dimensional power grid operation feature set; operation fault analysis is carried out, a fault feature vector group is generated, transfer learning is carried out, and a fault probability distribution matrix is generated; performing fault risk analysis according to the fault probability distribution matrix, and dividing a plurality of risk levels; and performing propagation path simulation of the target smart power grid on the plurality of fault early warning signals to generate a fault prediction report. According to the invention, the technical problems of low prediction precision and poor timeliness caused by fault prediction lagging and inaccurate fault propagation path simulation of the smart grid in the prior art are solved, and the technical effects of accurate prediction and risk early warning of the smart grid fault and improvement of the timeliness and accuracy of fault pre-judgment are realized.

The invention discloses a special equipment operation fault detection method and device, and relates to the related field of data processing, and the method comprises the steps: carrying out the dynamic sensing of special equipment, obtaining an operation dynamic data set of the special equipment, and synchronizing the operation dynamic data set to a long-short-term memory network, determining a plurality of fault influence factors, performing fault joint detection in combination with the equipment use scene parameter set, generating a first fault detection result, activating a fault probabilistic reasoning model, and performing fault reasoning in combination with the operation dynamic data set, and generating a second fault detection result and performing automatic comprehensive analysis and detection on the operation fault of the special equipment in combination with the first fault detection result. The technical problems of low detection efficiency and high maintenance cost caused by difficulty in processing large-scale data sets and real-time monitoring requirements in traditional fault detection are solved, an intelligent and automatic fault detection method is introduced, the accuracy and timeliness of fault detection are improved, and the technical effect of reducing risks and losses caused by equipment operation faults is achieved.

The invention discloses a decision boundary adaptive open set fault diagnosis method based on a domain adversarial neural network, and the method comprises the steps: S1, obtaining a rolling bearing fault data set under multiple working conditions and multiple fault states, and dividing the rolling bearing fault data set into source domain data and target domain data; s2, establishing a bearing fault diagnosis model, wherein the bearing fault diagnosis model can determine a decision boundary of a known fault state; performing iterative training on the bearing fault diagnosis model, and when the total loss function is converged, obtaining a trained bearing fault diagnosis model; the total loss function comprises a Bhatcharyya coefficient loss function, and the Bhatcharyya coefficient loss function is utilized to reduce the feature overlapping degree of different types of faults; and S3, based on the trained bearing fault diagnosis model, carrying out fault diagnosis on unknown faults in the actually collected bearing operation signals. According to the method, cross-working-condition open-set fault diagnosis can be realized, and the fault diagnosis capability is remarkably improved by ensuring accurate identification of unknown faults.

The invention discloses a self-adaptive dynamic fusion method and system for heterogeneous data, and relates to the technical field of data processing, and the method comprises the steps: obtaining multi-source heterogeneous data of power equipment; detecting the state of the equipment and judging whether the equipment is in a fault mode, and recording a fault feature vector if the equipment is in the fault mode; inputting the fault feature vectors into a fault resistance evaluation model, evaluating the fault resistance of each data source, and outputting a plurality of fault resistance indexes; setting a preset fault resistance index, and dividing the data into a first type and a second type of heterogeneous data; and configuring fusion weights for the two types of data, performing data fusion according to the weights, and outputting a fusion result. The technical problem that in the prior art, the reliability difference of multi-source heterogeneous data cannot be effectively recognized in the power equipment fault state, and consequently the fault diagnosis accuracy is poor is solved, and the purposes of dynamically optimizing data fusion, improving the data reliability and improving the fault diagnosis accuracy are achieved through fault resistance evaluation and a self-adaptive sampling strategy. Therefore, the fault diagnosis accuracy is improved.

The invention discloses a server fault processing method and device and a medium, and relates to the technical field of computers. And the server fault analysis problem is converted to obtain vector information, and the vector information is input to the pre-training model for reasoning analysis, so that accurate fault positioning is realized, and the search accuracy of fault analysis is improved. The fault analysis problem comprises a potential fault analysis problem, so that unknown faults are predicted and reasoned, and the fault processing delay is reduced. In inference analysis of a pre-training model, corresponding target inference strategies are matched based on priorities of different fault modes and association degrees of multiple faults instead of being directly processed one by one according to the priorities of the faults, and the association degrees of the multiple faults and whether the corresponding inference strategies are conflicted or not are concerned firstly; according to the method, the situation that secondary faults are caused by contradictory occurrence of heavy faults under the condition that the faults with low priorities are processed in a delayed mode is avoided, the fault maintenance progress is further accelerated, and the reliability of the server is improved.

The invention provides a rotor-bearing system fault diagnosis method and system based on virtual-real fusion, and relates to the technical field of signal detection, and the method comprises the steps: deducing a mixed eccentric unbalanced magnetic pull expression, and building a rotor-bearing system twinborn model in combination with a Hertz contact theory; introducing rotor eccentricity and bearing inner / outer ring faults, and constructing a multi-fault working condition twinborn model; performing multi-parameter identification on the model through an improved genetic algorithm (introducing a feature sensitivity evaluation factor, a self-adaptive crossover mutation probability and simulated annealing) to obtain a corrected twin model; generating a twin fault sample based on the correction model, and training by using a one-dimensional cyclic generative adversarial network (designing a comprehensive loss function) with a classifier to generate a sample close to real distribution; and virtual and real fusion samples form a balanced data set, and fault classification is realized through one-dimensional convolutional neural network training. According to the method, the problems of fault data shortage, large difference between a twin model and real data and the like are solved, and the fault diagnosis precision and reliability are improved.

The invention discloses an abnormal operation state detection method and system for waste gas purification equipment, and relates to the field of fault detection. The method is applied to the detection equipment and comprises the following steps: acquiring multi-source sensor data of the purification equipment; calculating a fluctuation index of the multi-source sensor data, and updating the fluctuation index to a preset fault influence relation graph; determining a plurality of abnormal equipment nodes according to the fluctuation indexes of the plurality of equipment nodes; traversing the fault influence relation graph by taking the plurality of abnormal equipment nodes as starting points to obtain a plurality of fault chains; calculating chain responsibility degrees of the plurality of fault chains, and determining the fault chain with the maximum chain responsibility degree as a root cause result; and the root cause result is output to an equipment management interface, so that troubleshooting personnel can rapidly troubleshoot abnormal causes. By implementing the technical scheme provided by the invention, the problem of low troubleshooting efficiency caused by various and complex fault types of the waste gas purification equipment is solved.

The invention discloses an air pre-heater cold end multi-fault diagnosis method and device based on a panorama and a dynamic intelligent model, and relates to the technical field of thermal power plant auxiliary engine fault diagnosis, and the method comprises the steps: constructing a panorama monitoring image of an air pre-heater rotor, and dividing the panorama monitoring image into a plurality of independent monitoring regions; extracting color channel information of each monitoring area, and dynamically updating a threshold range of each color channel based on a multi-trigger condition; obtaining morphological edge contour features and color channel deviation features, and jointly inputting the morphological edge contour features and the color channel deviation features into a pre-trained multi-fault classification model; the fault type recognition result, the confidence coefficient and the preset fault type weight are fused, the comprehensive degradation index of each monitoring area is calculated, a three-dimensional diagnosis result is output in combination with the spatial position information of the monitoring areas, and accurate positioning diagnosis, degradation trend quantification and multi-fault intelligent distinguishing of the cold end area of the air preheater are achieved. The method is suitable for full-life-cycle online monitoring and maintenance decision making of the rotary air pre-heater.

The invention discloses a method for intelligent line selection of a DC cable multi-circuit fault driven by a discharge signal, and relates to the technical field of power system and fault diagnosis thereof. The method comprises S1, extracting features and pattern recognition of collected discharge signals, S2, real-time monitoring of discharge signals in each circuit and obtaining fault initial state data, S3, constructing a classification model for multiple fault modes to improve recognition accuracy, and S4, intelligently classifying complex fault modes based on the classification model and selecting corresponding circuits; the method for intelligent line selection of a DC cable multi-circuit fault driven by a discharge signal can quickly identify fault points and select optimal solutions under a multi-circuit complex fault environment, avoids misjudgment caused by manual intervention, improves the overall stability and operation reliability of the power system, and solves the problems of poor recognition rate of complex fault modes, poor adaptability, low intelligence, and insufficient real-time performance and efficiency in the prior art.

The invention discloses a photovoltaic power station fault diagnosis method based on digital twinning and transfer learning, and belongs to the technical field of photovoltaic power generation fault detection. According to the method, a photovoltaic power station digital twin model based on a physical mechanism is constructed, operation behaviors in normal and multiple fault states are simulated, and a simulation data set with an accurate label is generated; in combination with the acquired real operation data, a fault diagnosis model is trained by adopting a transfer learning framework containing a domain adaptation loss item, so that the distribution difference between simulation data and real data is effectively reduced; a physical consistency loss item is innovatively introduced, and a physical rule is used as a constraint embedded model, so that the reliability and interpretability of a diagnosis result are improved; and finally, performing accurate fault diagnosis on the real-time operation data by using the trained model. According to the method, the model training problem caused by scarcity of real fault data is solved, the diagnosis precision and generalization ability are remarkably improved, and the method is suitable for intelligent operation and maintenance of the photovoltaic power station.

The invention provides a vehicle fault diagnosis method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining a fault code sequence of a vehicle, determining a target fault diagnosis path corresponding to the fault code sequence according to a target fault diagnosis map, determining a root cause fault part causing a target fault according to the target fault diagnosis path, and carrying out the fault diagnosis of the target fault. In addition, a fault diagnosis path in the target fault diagnosis atlas comprises a plurality of fault component nodes, a plurality of fault code nodes, a first logic type sub-path and a second logic type sub-path, and the first logic type sub-path and the second logic type sub-path are alternately connected in series. The first logic type sub-path is a path from a fault component node to a fault code node, and the second logic type sub-path is a path from the fault code node to the fault component node, so that fault diagnosis is carried out based on the target fault diagnosis map and the fault code sequence; accurate positioning of vehicle fault components in a multi-fault code scene can be realized.

The invention discloses an inverter fault feature intelligent diagnosis and prediction method and system, and the method comprises the following steps: collecting inverter operation data from a plurality of monitoring points, carrying out the preprocessing of a data vector, extracting features, combining the extracted features into a feature vector, constructing and training an intelligent diagnosis model, and recognizing an inverter fault mode. And correcting a diagnosis result by combining with an expert system rule, outputting a fault diagnosis result vector and a credibility value, predicting a future fault development trend, calculating a prediction fault probability and setting an early warning threshold. Therefore, various technical advantages can be fused, multi-monitoring-point data are collected, preprocessed and subjected to feature extraction, an intelligent diagnosis model is input, a result is corrected in combination with expert system rules, diagnosis accuracy and reliability are improved, the fault trend can be predicted, early warning can be set, operation and maintenance personnel can deal with and adapt to various fault types in time, and the fault diagnosis accuracy and reliability are improved. The overall intelligent level is enhanced, and the fault diagnosis prediction process is simply and efficiently realized.

The invention provides a fault analysis method and device of FTTR equipment, equipment and a medium, and relates to the technical field of artificial intelligence. The method comprises the following steps: firstly, acquiring a plurality of pieces of fault related data obtained by performing data acquisition on target FTTR equipment; secondly, semantic mining is conducted on the multiple pieces of fault related data, and target fault semantic representation is formed; and then, based on the target fault semantic representation, performing fault analysis on the target FTTR equipment, and outputting fault prediction data corresponding to the target FTTR equipment. Based on the above content, the problem that it is difficult to effectively analyze the LAN port fault in the prior art can be improved.

The invention relates to the technical field of fluid sealing, in particular to a PINN-based mechanical sealing fault diagnosis method and device, and the method comprises the steps: embedding a multi-physical field coupling simulation model, meeting a preset condition, of a target mechanical sealing system into a loss function of a physical information neural network (PINN), so as to construct an initial PINN fault diagnosis model, and inputting actual monitoring data of a plurality of sensors of the target mechanical sealing system into the initial PINN fault diagnosis model to output a plurality of fault parameters to be identified, further updating the initial PINN fault diagnosis model, generating a target PINN fault diagnosis model, and diagnosing the sealing state of the target mechanical sealing system according to the target PINN fault diagnosis model. And generating a fault diagnosis result. Therefore, the problems that in the prior art, in the face of complex working conditions and variable environments, the sealing performance cannot be comprehensively evaluated in real time, and consequently the accuracy and timeliness of mechanical sealing fault diagnosis are insufficient are solved.

The invention discloses a power grid fault prediction processing method and system based on panoramic situation awareness, and belongs to the field of power grid fault prediction, and the method comprises the steps: carrying out the data collection of a target power grid through a panoramic situation awareness system, and obtaining a multi-source perception data set; building a power grid space topological structure; clustering each node in the power grid spatial topological structure, and outputting a multi-layer topological network; constructing an intra-layer fault propagation model and a cross-layer fault transfer model; connecting a panoramic situation awareness system, and acquiring real-time power grid state data of the target power grid; and performing fault prediction on the real-time power grid state data, and outputting a plurality of pieces of fault early warning information. According to the method and the device, the technical problem of insufficient fault early warning accuracy and timeliness caused by lack of multi-level dynamic analysis and cross-layer influence consideration in power grid fault prediction in the prior art is solved, and the technical effect of improving the accuracy and timeliness of power grid fault prediction through intra-layer fault propagation and cross-layer fault transfer of a multi-layer topology network is achieved.

The invention discloses a fault analysis method, device and equipment for a rotary drilling rig and a medium, and belongs to the technical field of engineering machinery technologies. The fault analysis method for the rotary drilling rig comprises the following steps: acquiring equipment parameters of the rotary drilling rig, and respectively establishing corresponding mathematical models for a plurality of functional systems of the rotary drilling rig according to the equipment parameters; wherein the functional system comprises a mechanical system, a hydraulic system, a control system and a power system; establishing a model library by utilizing the mathematical model; the simulation models corresponding to all the function systems are connected based on the model library, and a digital prototype of the rotary drilling rig is obtained; the digital prototype is controlled to operate according to multiple fault working conditions, and the component energy loss of the digital prototype under each fault working condition is obtained; and establishing a mapping relationship between the energy loss of the component and the fault type of the fault working condition, and performing fault analysis on the rotary drilling rig according to the mapping relationship. The fault analysis precision and efficiency of the rotary drilling rig can be improved.

The invention discloses a ship electric propulsion system operation protection method and system, and the method comprises the steps: generating a time sequence signal set through a real-time operation data set of a ship electric propulsion system, constructing a plurality of fault monitoring models through an HSMM algorithm, processing different time sequence signals, and enhancing the monitoring capability of the system for multi-dimensional faults; the fuzzy processing is carried out on the system operation state through the membership degree function, effective fuzzy evaluation on the operation state of the ship electric propulsion system can be realized, reasonable judgment and decision can still be made in the face of partial inaccurate or incomplete data, and compared with traditional simple threshold value judgment, the method has the advantages that the judgment accuracy is improved, and the judgment efficiency is improved. The fuzzy rule can make more reasonable judgment in the state transition stage, misjudgment caused by boundary fuzziness is reduced, the fault monitoring accuracy is improved, the most appropriate fault protection scheme can be further matched according to the fault evaluation result, and it is ensured that the ship electric propulsion system is pertinently and effectively protected.

The invention discloses a flexible AC / DC hybrid power distribution network fault recovery optimization method and system based on multi-agent deep reinforcement learning, a computer device and a non-transient computer readable storage medium, and the method comprises the steps: firstly constructing an AC / DC power distribution network model considering the operation characteristics of a flexible switch and a voltage source converter; then, flexible equipment is modeled as an independent agent, a multi-agent training architecture is established, a composite reward function fusing a load recovery rate, voltage stability and operation safety is designed, and a mechanism combining centralized training and distributed execution is adopted to realize multi-point cooperative control between the flexible equipment; and finally, designing a recovery optimization process for rapid decision and control execution after the fault. The method can realize higher load recovery rate and better voltage control performance in various fault scenes, has the advantages of strong decision real-time performance, good system expandability, strong strategy generalization ability and the like, and is suitable for autonomous recovery and intelligent regulation and control of the novel flexible AC / DC hybrid power distribution network.

The invention provides a multi-path fault recovery method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining state data of storage equipment associated with a plurality of fault paths in a target storage system, and detecting whether the plurality of fault paths are recovered to a normal state or not based on the state data; when it is detected that the path state of the target fault path is recovered, performing multi-stage verification processing on the target fault path; if the target fault path does not pass the multi-stage verification, performing path repair operation on the target fault path which does not pass the multi-stage verification based on the non-passed verification type; and registering the repaired target fault path and the target fault path passing the multi-stage verification to an active path pool. Compared with the prior art, when it is detected that the path state of the target fault path is recovered, multi-stage verification processing is performed on the target fault path, and whether the recovered path has potential problems or not can be comprehensively and meticulously checked.

The invention provides a crown block operation fault detection method and system, electronic equipment and a storage medium, and belongs to the technical field of fault detection, and the method comprises the steps: detecting a crown block based on a first detection frequency, and obtaining target state detection data; and if the target state detection data does not meet the first condition, detecting operation data of the crown block based on the target state detection data to obtain crown block operation detection data. And inputting the crown block operation detection data and the target state detection data into the crown block fault detection model to generate a detection result. The crown block fault detection model is obtained by training crown block fault sample data and comprises a first correlation coefficient and a second correlation coefficient. The first correlation coefficient represents the correlation between each fault feature in the crown block fault sample data and the crown block fault type, and the second correlation coefficient represents the correlation among a plurality of fault features in the crown block fault sample data. According to the invention, the accuracy of crown block fault detection can be improved.

The invention discloses an elevator electromechanical fault diagnosis system based on a dynamic focusing network and an automatic trimming capsule network, and the system comprises a multi-mode data collection module which is provided with a vibration sensor, a temperature sensor and a displacement sensor, and collects the operation signals of an elevator traction machine, a brake and a steel wire rope in real time; the dynamic focusing network module is used for carrying out adaptive frequency band selection on the input signal and inhibiting noise interference; the automatic trimming capsule network module is used for realizing decoupling and classification of multiple fault features through a dynamic routing and capsule trimming mechanism; the federal learning coordination module is used for aggregating local model parameters of a plurality of elevator manufacturers and generating a global diagnosis model; and the diagnosis output module is used for outputting the fault type, the confidence coefficient and the residual service life prediction result, and triggering a maintenance alarm. Dynamic spectrum focusing is achieved, a self-adaptive frequency band attention mechanism is introduced into elevator fault diagnosis for the first time, and the noise suppression effect is improved by 42% compared with traditional band-pass filtering; the capsule is dynamically trimmed, the intensity threshold is activated to dynamically optimize the network structure, the multi-fault classification accuracy is improved to 96.5%, and the problem of cross-manufacturer data islands is solved.

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 relates to a doubly-fed induction generator (DFIG) fault detection and early warning method based on multi-scale latent variable regression (MSLVR), is suitable for multiple fault types and complex coupling effect scenes, and belongs to the technical field of fault detection. According to the method, firstly, key coupling variables influencing the DFIG are analyzed and selected from the angle of information theory, and the time scale is expanded through multivariate variational mode decomposition (MVMD), so that the data quality is optimized. Then, aiming at the defect that latent variable regression (LVR) is difficult to pay attention to the local influence of a sub-mode in multi-scale data processing, designing a serial architecture to gradually extract and eliminate interference of different components; and the residual subspace is processed by further combining principal component analysis (PCA), so that the online detection method capable of identifying early DFIG faults is developed. Through training and optimization, the model is finally stored for calling, the influence of the coupling effect on the DFIG can be effectively eliminated, efficient and accurate fault detection and early warning can be realized in various fault type scenes, and the detection accuracy is obviously superior to that of the existing mainstream method.

The invention discloses a comprehensive management and optimization system of an industrial internet, and relates to the technical field of industrial equipment management.The historical fault data of a plurality of continuous historical periods are extracted from a maintenance transmission log and are analyzed and processed to obtain a historical high fault set; performing high fault frequency value analysis processing on a plurality of fault high-frequency periods in a historical high fault set to obtain a sub-period comparison value, so as to reflect the overall characteristics of the same-rank historical high-frequency sub-periods after different fault high-frequency periods are divided in two aspects of fault frequency variation stability and trend similarity through the sub-period comparison value; the fault rule of the historical high-frequency sub-time periods with the same ranking after different fault high-frequency time periods are divided can be identified, and data support is provided for maintenance of industrial equipment in a future period.