1162 results about "Predictive maintenance" patented technology

The present invention relates to the field of industrial intelligent predictive maintenance, and in particular to an interpretable deep feature fusion network-based industrial intelligent predictive maintenance method, comprising: acquiring gearbox vibration data comprising noise; performing preliminary extraction and noise suppression on features of the acquired data by establishing an interpretable feature extraction module having a physical information constraint; integrating multi-scale features comprising long-distance and local dependencies by means of a dual-branch feature fusion module having global and local feature fusion capabilities; performing dimensionality reduction on a high-dimensional feature and generating an output by means of a classifier to obtain a final fault identification result; and performing interpretability analysis on a diagnosis process of a model. In the present invention, by embedding the signal processing technology having a well-defined physical theory support into a deep neural network, the interpretability and reliability of model inference results are effectively improved while the fault identification accuracy of the model is improved.

The invention discloses a power equipment dynamic health degree assessment and predictive maintenance method, and belongs to the technical field of railway power system operation and maintenance. The method comprises the following steps: constructing a parameterized digital twinborn body of power equipment, and collecting real-time operation data, resume data and environment data; based on the parameterized digital twins and the collected data, equipment health degree components are calculated through a multi-model cooperation method, and a comprehensive health index is generated through fusion; performing equipment life prediction and maintenance decision generation according to the comprehensive health index, and outputting an optimal maintenance strategy; and performing visual virtual rehearsal and augmented reality auxiliary execution on the optimal maintenance strategy to form a closed-loop maintenance system. According to the method, the problems of data and model separation, model static stiffness and health assessment deficiency in the prior art are solved, dynamic perception, accurate assessment and predictive maintenance of the equipment state are realized, and the operation and maintenance efficiency and the system reliability are improved.

The invention discloses an elevator generation type health state assessment method and system based on knowledge enhancement retrieval, and the method comprises the following steps: 1) collecting multi-source operation and maintenance data of an elevator, constructing an elevator health state knowledge graph containing equipment, parts, faults, health indexes and maintenance standards, and forming a structured health assessment knowledge base; 2) performing semantic analysis on operation indexes and natural language task description based on a large language model, and generating a health evidence sub-graph in combination with knowledge graph retrieval; 3) carrying out quantitative calculation on the component risk value and the complete machine comprehensive risk value under a rule engine and multi-agent framework, and constructing a memory graph to carry out historical trend analysis and similar case retrieval; and 4) generating an interpretable elevator health state evaluation report in combination with the logical reasoning graph and the standard knowledge graph. According to the method, the accuracy and traceability of elevator health assessment can be improved, and technical support is provided for predictive maintenance of the elevator.

The invention belongs to the technical field of flight simulator maintenance, particularly relates to a flight simulator predictive maintenance method based on machine learning, and solves the problems that existing maintenance depends on regular inspection and passive maintenance, fault early warning lags behind, and the false and missing report rate is high. The method comprises the following steps: acquiring historical operation data, sensor time sequence data, fault records and environmental parameters of a flight simulator, and carrying out cleaning, labeling and feature fusion preprocessing on the historical operation data, the sensor time sequence data, the fault records and the environmental parameters; constructing a composite health feature set containing statistical features, dynamic health state values and aerial material reliability parameters; a mixed prediction model (random forest feature screening + LSTM time sequence prediction + adaptive correction reliability evaluation) is adopted to train a model, prediction result fusion analysis and multistage decision rule post-processing are combined, and a maintenance work order and a spare part demand plan are output. According to the method, the accuracy and timeliness of fault prediction are improved, the maintenance conversion from passive response to active pre-judgment is realized, and the maintenance cost and the non-planned shutdown risk are greatly reduced.

The invention discloses a dynamic closed-loop management method for bridge-shaped contact performance, and relates to the technical field of power system protection. The method comprises the following steps: S1, a dynamic sensing process: acquiring multi-source dynamic parameters of a bridge-shaped contact in a non-intrusive mode; s2, a quantitative evaluation process: fusing the multi-source dynamic parameters and the static parameters, and calculating a health index or a health level of the contact through a preset model; s3, adaptively optimizing the process, dynamically adjusting the closing energy or protection parameters of the circuit breaker according to the health state, and compensating the performance change of the contact; and S4, a predictive maintenance process: performing trend extrapolation based on the historical data of the health index, predicting the residual life and generating a maintenance suggestion. The processes are sequentially connected and cyclically iterated to form dynamic closed-loop management, so that the problems of one-sided parameter sensing, inaccurate evaluation, fixed parameters and maintenance lag in the prior art are solved, the operation reliability of the bridge-shaped contact is improved, the service life of equipment is prolonged, and the operation and maintenance cost is reduced.

The invention discloses an energy storage battery health feature extraction and state evaluation method based on transfer learning. The method comprises the following steps: S1, constructing a source domain health feature library and pre-training a model; according to the method, dependence on complete cyclic data is broken through, high precision and robustness are still achieved under the conditions of data sparsity and working condition difference, and the method is suitable for intelligent operation and maintenance and predictive maintenance of an energy storage power station. And meanwhile, common incomplete and partial charge and discharge data fragments under actual working conditions can be directly utilized for feature extraction and state evaluation, dependence on complete charge and discharge cycles is avoided, and the application scene of the data driving method is greatly widened.

The invention relates to the technical field of computers, discloses a multi-system data fusion predictive maintenance method and system, and aims to solve the problems that multi-source heterogeneous data integration is difficult, health state perception is one-sided, prediction model adaptability is poor and maintenance decision closed loops are missing. The method comprises the following steps: acquiring mechanical vibration, thermodynamics and electrical signals through a multi-source sensing access module, and performing time alignment; and in combination with operation behavior information input by the operation registration terminal, a data fusion engine executes standardized mapping and missing value compensation to generate a structured input matrix. According to the scheme, deep fusion of multi-system data and continuous quantitative evaluation of the health state are achieved, the fault early warning advance time is shortened, the evaluation accuracy is improved, adaptive regulation and control and closed-loop maintenance decision are supported, the service life of key components is prolonged, and the method is suitable for various high-end manufacturing scenes.

The invention discloses a photovoltaic module service life prediction method and a health management system. The life prediction method comprises the following steps: acquiring degradation data of a photovoltaic module, and segmenting the degradation data based on a sliding window; reconstructing the degradation data in each sliding window in combination with Kalman filtering and an RTS smoothing algorithm to obtain a degradation track; according to the degradation track, based on a degradation model and a preset failure threshold value, probability distribution of the remaining life of the photovoltaic module is determined; wherein the degradation model is constructed based on a Wiener process, and posterior distribution of parameters is estimated by using a variational inference method. According to the invention, accurate evaluation of the running state of the photovoltaic module and high-precision prediction of the residual life can be realized, so that reliable support is provided for intelligent operation and maintenance and predictive maintenance of a photovoltaic power station.

The invention relates to the technical field of cable asset health management and predictive maintenance, and discloses a cable discharge signal blind separation and enhancement processing method based on an adversarial network, and the method comprises the steps: building a multi-modal monitoring data set through collecting mixed signals and environment data in cable operation; blind separation of discharge signals is realized by using the generative adversarial network, and prior information is not needed; identifying the number of potential signal sources through covariance analysis and double-criterion estimation; iterative optimization and signal enhancement are carried out in combination with a graph neural network and variational reasoning; and finally, through multiple cross validation and quality correction, an enhanced signal with high reliability is output. According to the method, the signal processing technology is deeply fused with asset management, risk prediction and operation and maintenance decision, weak discharge signals can be effectively separated and enhanced under the condition of low signal-to-noise ratio, the accuracy and reliability of cable early fault diagnosis are improved, and credible data support is provided for cable asset health state assessment, risk prediction and operation and maintenance decision.

The invention relates to the technical field of intelligent detection, and discloses a building quality evaluation method and system based on concrete nondestructive testing and a storage medium. The method comprises the steps that a piezoelectric ceramic sensor array is arranged to collect micro-vibration response signals, and an original vibration data set is obtained; extracting an energy distribution coefficient of each frequency band by using a wavelet packet decomposition algorithm, and constructing a damage feature vector matrix; establishing a physical constraint neural network model, and outputting a damage variable time sequence; fusing the damage variable with ultrasonic and rebound data, and calculating comprehensive strength and damage degree indexes; and calculating the remaining service life by using a time sequence prediction algorithm, and generating an evaluation report. According to the method, the technical problem that the existing concrete nondestructive testing technology cannot realize microstructure damage evolution dynamic monitoring and residual life prediction is solved, and the accuracy of building quality evaluation and the scientificity of predictive maintenance decision are improved.

The invention discloses an edge cloud collaborative industrial equipment health management and predictive maintenance method, and the method comprises the steps: forming a closed-loop system through four deep coupling steps: edge adaptive fusion perception, cloud knowledge enhancement reasoning, edge cloud collaborative self-evolution prediction, and risk-driven maintenance decision. The edge end dynamically adjusts a sensor acquisition and feature fusion strategy according to the equipment health state, the cloud end performs causal reasoning and situation evaluation by using a physical-data mixed knowledge graph, and the edge cloud collaboratively optimizes a prediction model through federal element learning and bidirectional knowledge distillation; maintenance decision is based on multi-objective optimization, the actual effect is fed back to the perception and prediction link, the method achieves accurate assessment of the equipment health state, early fault prediction and maintenance intelligent decision, the availability of the equipment is remarkably improved, the maintenance cost is reduced, and core technical support is provided for intelligent manufacturing.

The invention relates to the technical field of power station operation and maintenance, and discloses a new energy power station intelligent operation and maintenance system based on big data analysis. The system comprises a data acquisition module used for acquiring and preprocessing operation data and maintenance logs of power station equipment; the diagnosis evidence generation module is used for generating diagnosis evidences of three dimensions of performance degradation, mechanical abnormity and repeated fault risk through parallel analysis; the data analysis module maps the diagnosis evidence to a state space to construct an equipment health track, calculates a health index based on a mahalanobis distance and predicts a change trend; the operation and maintenance decision module is used for performing significance verification on the health trend, generating a dynamic risk score, automatically matching a maintenance strategy and establishing a feedback optimization mechanism; according to the invention, accurate evaluation and predictive maintenance of the equipment health state are realized, and the operation and maintenance efficiency and the equipment reliability of the new energy power station are effectively improved.

The invention discloses a lithium battery SOH and RUL prediction system and method based on multi-scale feature federation, and relates to the technical field of battery detection, the system comprises a data acquisition and feature extraction module, an encryption and uploading module, a model construction module, a recursion updating module and a work order generation module; through the multi-scale feature fusion formula, the time sequence features and the statistical features of the lithium battery are organically combined, the fusion feature vector is generated, degradation information of the lithium battery under different time scales and operation conditions is comprehensively captured, the health state of the lithium battery and the accuracy of prediction of the remaining service life of the lithium battery are improved, and the prediction accuracy of the remaining service life of the lithium battery is improved. And meanwhile, through dynamic Top-K sparse compression, 8-bit linear quantization and AES-256-GCM encryption technologies and in combination with a differential privacy protection mechanism, the data transmission quantity is reduced, the data security is enhanced, sensitive information leakage is prevented, and a reliable guarantee is provided for remote monitoring and predictive maintenance of the lithium battery.

The invention discloses a power equipment health evaluation method and system based on time sequence analysis and probability modeling, equipment and a medium, and relates to the technical field of power equipment state monitoring and fault prediction, and the method comprises the steps: obtaining and preprocessing multi-source operation data of power equipment, outputting a predicted value and a confidence interval of a future parameter through a time sequence prediction model, calculating a residual sequence of an actual observation value and a predicted value, fitting distribution through a probability distribution model, establishing a statistical characteristic model of a normal operation state of the equipment, performing anomaly judgment, calculating a health degree index of the equipment based on a deviation degree and a dynamic weight of a monitoring parameter and weighted accumulation, and dividing equipment state grades according to the index. Quantitative evaluation of the health state of the equipment is realized. According to the method, accurate quantification and early abnormity identification of the health state of the power equipment are realized, a reliable basis is provided for predictive maintenance, and the intelligent level and the safety guarantee capability of power grid operation and maintenance are remarkably improved.

The invention discloses a key parameter long-time-sequence prediction method for a complex process industry, and the method comprises the steps: collecting multivariable sensor data in the process industry, and constructing a high-dimensional long-time-sequence prediction data set; constructing a PatchConvRNN prediction model by combining time slice embedding, dimension decoupling convolution, depth separable convolution and a recurrent neural network based on a sequence-to-sequence normal form; a point value-statistical mixed loss function is adopted, the point prediction precision, the sequence mean value and the standard deviation consistency are optimized at the same time, a prediction model is trained in combination with an optimization algorithm, and network model parameters are adjusted; and comprehensively evaluating the prediction model through a root-mean-square error, an average absolute percentage error and a standard deviation average absolute error. According to the method, high-precision prediction and fluctuation maintenance of the key time sequence variables under the complex working condition of the industrial process are achieved, and powerful support is provided for quality control and predictive maintenance of the production process.

The invention belongs to the technical field of compressor maintenance, and discloses a compressor predictive maintenance system and method based on multi-source data fusion, and the method comprises the steps: carrying out the heterogeneous data time-frequency feature analysis of compressor signal data, and generating a multi-dimensional feature spectrum; carrying out load-dependent fault signal separation to generate load decoupling fault feature data; performing environment interference factor elimination analysis to generate a pure fault feature matrix; performing cross-domain signal correlation mapping to generate a multi-source data fusion mode map; constructing a fault feature propagation link, and generating fault evolution path data; carrying out degradation trend prediction under a variable load condition, and generating fault development situation data; constructing a dynamic threshold self-adaptive early warning model, and generating fault early warning critical value data; health state comprehensive evaluation is carried out, a compressor state report is generated, and predictive maintenance implementation is executed; according to the invention, accurate recognition and prediction of the compressor fault are realized, and the equipment reliability and maintenance efficiency are improved.

The invention relates to an equipment predictive maintenance management method and system based on multi-feature fusion, belongs to the technical field of equipment health management, and is used for solving the problems that existing sensor data is easily disturbed and distorted, and a potential causal structure of an equipment degradation path is difficult to reveal due to lack of a comprehensive modeling framework. The method comprises the steps of collecting multi-source data in real time, extracting a causal contribution degree of the data to a fault to generate a causal significance feature value, generating a three-dimensional health feature vector through an anti-fact neural network model in combination with a physical failure model, fusing the two to obtain an equipment state risk feature matrix, inputting the model to output a risk score, and obtaining an equipment state risk result. And finally, dynamically adjusting the monitoring frequency and the maintenance level and iteratively optimizing the strategy. According to the method, data interference can be filtered out, multi-class reasoning mechanisms are deeply fused, the equipment degradation law is accurately revealed, and full-life-cycle self-adaptive maintenance is achieved.

The invention provides a flange assembly predictive maintenance method based on residual life distribution dynamic identification, which comprises the following steps: firstly, establishing a linear Wiener model of a flange assembly degradation process, and designing a Bayesian parameter dynamic updating mechanism based on normal-inverse gamma conjugate prior; secondly, deducing residual life complete probability distribution considering parameter uncertainty through a Monte Carlo sampling method, overcoming the limitation of point prediction, and providing a maintenance decision rule based on a time-probability threshold; and finally, establishing a decision parameter optimization model with the goal of minimizing the long-term average cost rate, and solving an optimal maintenance strategy through system simulation. Compared with the prior art, the residual life prediction accuracy is remarkably improved, the maintenance cost and the equipment reliability are effectively balanced through a probabilistic decision-making mechanism, the full-life-cycle maintenance cost is remarkably reduced while the flange sealing safety is ensured, and the method has important popularization value in engineering equipment predictive maintenance.

The invention provides a pressure vessel full life cycle stress fatigue assessment and predictive maintenance system based on digital twinning. The system comprises a model construction module used for constructing a three-dimensional digital twinning model corresponding to a solid pressure vessel; the data synchronization module is used for integrating historical test data and actual operation load data of the solid pressure vessel into the three-dimensional digital twinborn model and simulating dynamic stress distribution of the solid pressure vessel in an actual operation load process; the evaluation and maintenance module is used for evaluating the accumulated fatigue damage of the solid pressure vessel and the residual fatigue life in the current state based on the dynamic stress distribution, and determining a potential risk area and a risk time window of the solid pressure vessel based on an evaluation result; and on-demand inspection plans and predictive maintenance strategies of the entity pressure vessels are generated based on the potential risk areas and the risk time windows. Stress fatigue evaluation of the whole life cycle is achieved, and the accuracy and reliability of life prediction are remarkably improved.

The invention relates to the technical field of power system state monitoring and operation and maintenance, and discloses a power distribution network equipment state intelligent sensing and predictive maintenance decision-making method, system, equipment and medium, and the method comprises the steps: collecting equipment operation data, monitoring data and environment data through a power distribution automation system, an online monitoring device, an environment sensor and other channels; performing time synchronization and quality verification on the multi-source data; extracting multi-dimensional characteristic parameters reflecting the running state of the equipment, and performing normalization processing and fusion analysis on the characteristics; establishing an equipment state evaluation model by using a deep learning algorithm, and carrying out quantitative scoring on the current health condition of the equipment; constructing a time sequence prediction model, and predicting the future degradation trend and residual life of the equipment; and establishing a multi-objective optimization model, and generating an optimal maintenance decision scheme. The whole process is from data acquisition, state perception and fault prediction to decision optimization, and intelligent management of power distribution network equipment is realized.

The invention provides a high and low voltage power distribution cabinet remote operation and maintenance management and control system based on cloud computing, relates to the field of remote operation and maintenance management, and improves the accuracy and operation and maintenance efficiency of power distribution cabinet fault early warning. The method comprises the following steps: firstly, acquiring sensing data through an edge intelligent sensing module, performing multi-dimensional correlation analysis based on a static safety threshold, identifying an abnormal trend with spatial-temporal correlation, and generating a preliminary early warning; then, a cloud platform prediction decision module drives the digital twin model to carry out dynamic simulation, and a predictive maintenance strategy and a dynamic safety threshold are generated in combination with a prediction algorithm of a historical performance decline curve; and finally, the adaptive control execution module optimizes subsequent analysis by using a dynamic threshold value, and triggers a differential control action from monitoring adjustment to emergency isolation according to a strategy type. According to the invention, by constructing the closed-loop intelligent operation and maintenance architecture of cloud-side cooperation, the operation and maintenance mode transformation from passive response to active prediction is realized, and the reliability and safety of the operation of the power distribution system are significantly improved.

The invention discloses an industrial equipment operation state monitoring and abnormity early warning method and system based on acoustic characteristics. The system comprises a multi-channel acoustic data acquisition module, an acoustic data preprocessing and enhancing module, a robustness acoustic feature extraction module, a deep learning abnormal mode recognition module, an abnormal early warning and auxiliary positioning module, an intelligent diagnosis and early warning enhancing module and a data storage and management module. The method comprises the following steps: collecting multichannel acoustic data, and extracting time domain, frequency domain and time-frequency domain robustness features after noise reduction, screening and framing preprocessing; identifying equipment abnormity through a deep learning model; a microphone array is combined to analyze the sound source direction and position abnormal equipment, and early warning information is generated; and integrating SCADA / DCS process data, and generating graded early warning and operation and maintenance suggestions. The method can adapt to a complex noise environment, accurately recognize early abnormity, provide intelligent diagnosis, support predictive maintenance and reduce fault risks.

The invention discloses a bus voltage multi-dimensional real-time monitoring and analyzing method, system, equipment and medium, and belongs to the technical field of power system monitoring and fault prediction.The method comprises the steps that high-frequency synchronous sampling is conducted on bus voltage signals, discrete time sequence voltage signals are obtained and preprocessed, preprocessed voltage signals are obtained, and the discrete time sequence voltage signals are obtained; the method comprises the following steps: synchronously extracting multi-dimensional characteristic parameters of voltage, obtaining a characteristic parameter set, calculating a health degree index comprehensively representing a voltage state by utilizing a dynamic weighted fusion algorithm based on the characteristic parameter set, calculating an abnormal degree of a current value according to a historical data sequence of the health degree index, and performing fault grade judgment according to the abnormal degree. And performing trend extrapolation based on the health degree index and the historical data sequence of the key parameters in the characteristic parameter set, and generating predictive maintenance suggestions. According to the method, the crossing from passive alarm to predictive maintenance is realized, and the intelligent level and the operation and maintenance reliability of state monitoring of the power system are remarkably improved.

The invention provides a grounding wire state monitoring and early warning system and method based on a random forest algorithm, relates to the technical field of electric power safety monitoring, and solves the limitation problems of low efficiency, poor data continuity, inaccurate early warning judgment and the like in the existing scheme. In the system, a multi-source sensor network acquires multi-dimensional operation data corresponding to a grounding wire and transmits the data to a data acquisition terminal; the data acquisition terminal performs feature extraction processing to generate a feature vector set and uploads the feature vector set to the cloud data processing platform; the cloud data processing platform uses a random forest classification model, based on the feature vector set, realizes dynamic feature weight adjustment through feature importance analysis, constructs a hierarchical decision tree structure, identifies and outputs a grounding wire fault mode, and issues the grounding wire fault mode to the local early warning terminal; and the local early warning terminal provides early warning information for the ground wire site by adopting a graded and differentiated early warning mode. According to the invention, high-precision early warning and predictive maintenance of the state of the grounding wire are effectively realized.

The invention relates to the technical field of digital twinning, in particular to a digital twinning-based heat dissipation system full-life-cycle predictive maintenance platform, which comprises a data acquisition module; a model construction module; the block division module is used for determining direct partitioning based on the regional coupling strength or related partitioning based on the heat exchange correlation degree according to the fault transfer coefficient; the category analysis module is used for determining a block category based on the temperature deviation degree and determining whether to adjust the block category of the second-class block based on the maximum fluctuation period attenuation rate according to the pollutant deposition influence degree and the thermal aging accumulation rate of the second-class block; the updating analysis module is used for determining the updating frequency of each updating block based on the comprehensive evaluation value; and the updating optimization module is used for determining whether to carry out optimization adjustment or not according to the block change coefficient. The model updating efficiency can be improved.

The invention provides a clutch abnormity detection method and device, and relates to the field of clutch state monitoring and fault diagnosis, and the method comprises the steps: obtaining the monitoring parameters of the operation state of a clutch; performing signal filtering and characteristic quantity extraction on the monitoring parameters to obtain characteristic quantities corresponding to the monitoring parameters; comparing each characteristic quantity with an abnormal threshold value configured in a corresponding operation mode, and determining whether a parameter abnormality and a corresponding target abnormal parameter type exist or not; and determining a target clutch fault type corresponding to the target abnormal parameter type based on the corresponding relationship between the configured different abnormal type combinations and different clutch fault types. According to the method, the accuracy and reliability of state monitoring are remarkably improved, and a key decision basis is provided for predictive maintenance.

The invention discloses a steel wire rope nondestructive testing method and system based on sensing and physical feature fusion, and belongs to the technical field of steel wire rope nondestructive testing. The method comprises the following steps: firstly, carrying out saturation magnetization on a steel wire rope through an axial Gaussian difference excitation probe so as to improve the signal-to-noise ratio of a defect magnetic flux leakage signal; and meanwhile, the instantaneous acceleration is synchronously acquired through the inertial measurement unit. And establishing a speed-signal model based on the law of electromagnetic induction, and performing dynamic speed compensation on the magnetic flux leakage original voltage signal. And then, quantitatively extracting characteristic parameters with clear physical significance, such as peak voltage, maximum gradient, full width at half maximum and the like, from the standardized magnetic flux leakage signal, fusing the characteristic parameters with deep characteristics extracted by the deep learning model, and inputting a fused characteristic vector into a full-connection neural network and a classifier to realize defect type identification and degree evaluation. According to the invention, high-precision and interpretable intelligent detection and residual life prediction of steel wire rope defects can be realized, the detection reliability is obviously improved, and predictive maintenance is supported.

The invention discloses an intelligent monitoring and evaluating system for the metal health state of a generator set, and belongs to the technical field of state monitoring of power generation equipment. The system comprises a data acquisition and preprocessing module, a data fusion and feature extraction module, a metal health state evaluation module, a residual life prediction and fault tracing module and a visual intelligent decision support module. The method comprises the following steps of: extracting cross-scale damage characteristics by fusing macroscopic operation data and microscopic nondestructive testing signals; calculating a comprehensive health index by adopting a physical mechanism model and data-driven model adaptive weighted fusion mode; dynamic residual life prediction and failure reason intelligent traceability are realized; and finally, graded early warning and maintenance decision suggestions are provided through a three-dimensional visual interface. According to the method, the problems of single monitoring dimension, model isolation and static prediction in the prior art are solved, and comprehensive, high-precision and interpretable evaluation and predictive maintenance support of the health state of the metal part are realized.

The invention relates to a conveying belt tearing positioning and tracing system based on RFID and multi-sensor fusion, which effectively overcomes the influence of belt slipping and elastic deformation through multi-sensor fusion and motion compensation, and realizes accurate positioning of a tearing point. Meanwhile, through cross validation of vibration, vision and infrared data, the recognition accuracy is improved to 99.5% or above, false alarms caused by environmental factors (such as dust and moisture) are greatly reduced, stable operation in a severe industrial environment is ensured, and high reliability and low false alarm rate are achieved. And thirdly, the system supports long-term data tracing and intelligent operation and maintenance, and a user can analyze a tearing trend and identify a high-frequency fault area by storing historical data of at least 180 days and providing a visual tool, so that a maintenance plan is optimized, predictive maintenance is realized, and faults are reduced from the source.

The invention discloses a wind generating set transmission chain fatigue load prediction method and system considering sandstorm characteristics, and belongs to the technical field of wind power generation and structure fatigue prediction. According to the method, a wind speed time sequence capable of representing sandstorm evolution characteristics is obtained and serves as external physical excitation to be input into a wind-machine-electricity coupling state space model, and a dynamic torque time sequence of a transmission chain low-speed shaft is obtained through calculation; on the basis, a load spectrum is generated by adopting a rain flow counting method, and the accumulated fatigue damage degree corresponding to the sandstorm event is calculated in combination with the Miner linear accumulated damage criterion and the S-N curve of the low-speed shaft material; and further comparing the damage degree with a preset threshold value, and when the damage degree exceeds the threshold value, automatically generating a preventive active load reduction control strategy aiming at the sandstorm, thereby realizing a closed loop of prediction and control. According to the method, the influence of extreme non-stable wind conditions such as sandstorm and the like on the fatigue damage of the transmission chain can be quantitatively evaluated, and technical support is provided for safe operation and predictive maintenance of a wind turbine generator in extreme weather.