1323 results about "Failure probability" patented technology

The invention discloses a predictive maintenance method for an optical storage and charging integrated power station based on deep learning, and the method comprises the steps: constructing an efficient equipment state evaluation and prediction model based on multi-source data fusion, an intelligent prediction algorithm and a closed-loop optimization feedback mechanism, collecting multi-source data, and carrying out the fusion processing, an improved Attention-LSTM model is utilized to evaluate and predict the state of equipment, a transfer learning method is adopted to improve generalization ability, Bayesian optimization and an adaptive sliding window technology are combined at the same time, dynamic threshold adjustment is performed, a deep reinforcement learning algorithm based on a Markov decision process is adopted to optimize a maintenance strategy, and the maintenance efficiency is improved. Weibull distribution is introduced for failure probability modeling, the maintenance cost and the fault risk are balanced, continuous optimization and dynamic adaptive adjustment of a predictive maintenance scheme are realized through a closed-loop feedback mechanism, the prediction accuracy and the intelligent level of maintenance decision are remarkably improved, planned maintenance and sudden fault maintenance are reduced, and the maintenance efficiency is improved. And the reliability of the charging station is improved.

The invention relates to the technical field of power system fault monitoring, and discloses a 10kV voltage transformer ground fault real-time monitoring method and system, and the system comprises a ground fault data collection module, a waveform distortion analysis module, a transient signal detection module and the like. Three-phase current, zero-sequence current and voltage waveform data are collected, a current harmonic component proportion, a zero-sequence current high-frequency transient energy value and the like are analyzed, a fault type is determined by combining a phase difference, and a fault position is positioned according to topological parameters and line impedance. And the temperature of the grounding down lead can be monitored, and the fault probability is evaluated and the threshold value is dynamically adjusted by machine learning. According to the invention, real-time accurate monitoring can be realized, fault types and positions can be accurately diagnosed, potential hidden dangers can be comprehensively monitored, the intelligence of a monitoring system is improved, the operation and maintenance cost and risks are effectively reduced, and safe and stable operation of the 10kV power distribution network is ensured.

The invention provides an industrial robot distributed collaborative debugging method and system, and relates to the technical field of distributed control, and the method comprises the steps: building a directed acyclic graph model of an industrial robot collaborative debugging task, analyzing the directed acyclic graph model through a critical path method, and determining a critical path of the collaborative debugging task; reliability modeling is carried out on each industrial robot node in the collaborative debugging task, and the failure probability of each collaborative debugging sub-task under the current scheduling scheme is calculated in combination with the time redundancy characteristics of each collaborative debugging sub-task; according to the obtained directed acyclic graph model of the industrial robot collaborative debugging task, the key path of the collaborative debugging task and the failure probability of each collaborative debugging sub-task under the current scheduling scheme, defining a state space of a task scheduling scheme, searching the state space through a dynamic planning algorithm, and scheduling the task scheduling scheme according to the searched state space; and obtaining a scheduling scheme with the minimum global failure probability as an optimal fault-tolerant scheduling strategy at the current moment.

The invention discloses a power semiconductor device aging on-line diagnosis method, which relates to the field of power electronics, and comprises the following steps: integrating an intelligent sensor and an aging test circuit, and establishing a multi-physics field coupling simulation model; based on the multi-physics field coupling simulation model, evaluating the aging state of the current device to obtain the health index of the device; based on the multi-dimensional data matrix, constructing an anomaly detection model, and outputting an anomaly detection result; predicting the failure probability and the remaining service life of the semiconductor device according to an abnormal detection result; generating a maintenance suggestion based on the predicted failure probability and remaining useful life of the semiconductor device; according to the method, the maintenance suggestion is generated according to the prediction result, the user is guided to take preventive maintenance measures in time, the accuracy of aging state evaluation is improved, the timeliness of fault early warning is enhanced, and economic losses and technical risks caused by sudden faults are reduced.

The invention discloses a power distribution network operation safety risk assessment method considering typhoon and rainstorm composite disasters, and the method comprises the following steps: generating different typhoon paths as main scenes based on the typhoon and rainstorm disaster forecast data, and endowing each main scene with a weight according to the occurrence probability; the influence of microtopography and rainfall intensity on the wind speed V (r) in the main scene is considered, the corrected wind speed is calculated, the total failure probability of the distribution line between the node i and the node j is calculated according to the fault probability of the distribution line between the node i and the node j and the failure probability caused by foreign matter hanging, and the power distribution equipment failure probability is calculated according to the water accumulation amount of the power distribution network area; simulating and generating N fault scenes s through a Monte Carlo method to form a fault scene set S, and endowing each fault scene with a weight; and taking the minimum expected total load loss amount in all fault scenes as an objective function, constructing a risk assessment model, and solving the risk assessment model in combination with constraint conditions.

The invention relates to the field of power grids, and particularly discloses a multi-type extreme weather oriented power system risk assessment method and device. Comprising the steps of defining risk scene elements, determining a risk scene judgment confidence value based on a threshold value under a power grid normal operation condition, constructing a full-scene comprehensive evaluation framework, establishing an extreme weather occurrence probability model, performing generation and screening by using an approximate daily search method and an approximate distance method, and obtaining a source load power probability model. A fault probability model under the influence of different factors is constructed, a series model is utilized, a comprehensive time-varying fault probability model is determined, a power grid fault state occurrence probability model is obtained, comprehensive analysis is carried out, a risk scene probability model is obtained, and a risk consequence index system is established according to a power grid system operation potential risk. And inputting the target power grid data into the risk scene probability model, and performing evaluation by using the risk consequence index system to obtain a risk evaluation result. And the disaster response capability of various aspects of a future power system can be improved.

The invention discloses a power distribution network overhead line risk refined early warning method and system. The method comprises the following steps: collecting meteorological time sequence data and power distribution network topology and line parameter data; and preprocessing the data, and constructing a prediction data set. And establishing a fault probability prediction model based on the meteorological data and the line parameters. Calculating the fault probability by using the mathematical model, correcting the model, training in combination with static topology and dynamic meteorological data, and constructing a fault probability network model. And analyzing the importance of the nodes and lines of the power distribution network, and evaluating the load loss proportion of the nodes. And carrying out normalization processing on the importance indexes. And comprehensively calculating the predicted fault probability, the node loss load and the importance index to obtain a branch risk parameter. Fine early warning is carried out according to a set early warning threshold value, and dynamic adjustment is carried out to adapt to the actual operation condition. The method has the advantages that comprehensive risk assessment is provided, and the accuracy of fault prediction and the timeliness of risk early warning are improved.

The invention relates to the technical field of pipeline stress fatigue detection, and discloses a natural gas station elbow tee joint stress fatigue digital intelligent analysis method and system and a product. The method comprises the following steps: establishing a multi-physics field coupling digital twinborn model of the elbow tee joint, integrating a geometric structure, material attributes and a fluid-solid coupling mechanism, and simulating flow-induced vibration stress and corrosion fatigue interaction; collecting real-time multi-source data of the elbow tee joint; real-time multi-source data is utilized to update boundary conditions and parameters of the digital twin model, and material constants are dynamically corrected through machine learning, so that self-calibration of the model is realized; performing stress distribution analysis based on the updated model to obtain stress field data, and performing fatigue crack propagation prediction in combination with real-time multi-source data to generate a prediction result; and based on the prediction result, evaluating fatigue failure risks, including crack growth rate, residual life evaluation and failure probability, and outputting alarm information or optimization decision information.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology and medical health, and discloses a processing environment switching and recovery method, device, equipment and medium. The method comprises the steps that multi-source heterogeneous data in a main processing environment and a standby processing environment are acquired, and the system fault probability is obtained through multi-model collaborative prediction; a dynamic threshold value is generated in combination with a historical service period mode and a real-time service load, when the fault probability exceeds the threshold value, a switching strategy is generated based on the fault scene knowledge base and the service priority, and flow scheduling between the main processing environment and the standby processing environment is executed; and monitoring the business index of the standby processing environment during the scheduling period, and triggering the fusing rollback when the business index is lower than the health standard. According to the method, the fault identification precision is improved through multi-source data fusion and multi-model prediction, adaptive scheduling is realized in combination with a dynamic threshold and a switching strategy, and fusing rollback is triggered to guarantee high availability and data consistency, so that the continuity and stability of key services are enhanced.

The invention discloses a safety monitoring and early warning method for a power system, and relates to the technical field of power monitoring, and the method comprises the steps: obtaining a target detection line and a real-time monitoring data set; performing task disassembly on the real-time monitoring data set, and screening an identification task set; establishing a fault probability resonance model, inputting the identification task set into the fault probability resonance model to obtain the fault probability of each power device, and calculating a resonance effect value between any two devices; obtaining a transfer matrix based on the fault probability and the resonance effect value of each power device; generating a probability graph based on the transfer matrix in combination with the time sequence; predicting a fault propagation path based on the probability graph, adjusting the fault probability of the power equipment, identifying a key path and risk equipment based on the adjusted fault probability, and generating a monitoring analysis report; the response efficiency of the monitoring data of the power transmission and transformation equipment is improved; the effect of dynamic correlation analysis between faults is achieved; and the effects of fault equipment and a fault propagation path are accurately predicted.

The invention relates to the technical field of equipment management, and discloses an industrial park equipment operation state management method and system. The method comprises the following steps: acquiring equipment basic information, historical fault data and real-time operation data of equipment; according to the basic information of the equipment, performing category analysis on the equipment to obtain an equipment category; according to the historical fault data and the equipment category, performing fault period prediction on the equipment based on a pre-trained support vector regression model to obtain an equipment fault period; according to the real-time operation data and the equipment category, performing health index calculation on the equipment to obtain an equipment health index; and according to the equipment category, the equipment fault period and the equipment health index, performing fault prediction to obtain a predicted fault probability, and sending the predicted fault probability to an operation and maintenance end, so that the operation and maintenance end performs equipment maintenance according to the predicted fault probability. According to the method, the equipment management efficiency of the industrial park can be improved, and the equipment state of the industrial park can be effectively analyzed and managed.

The invention discloses a wiring harness dynamic load and electromagnetic interference coupling test method, and relates to the technical field of wiring harness testing, and the method comprises the steps: S1, defining a plurality of environment parameter groups, and constructing a test platform; s2, dynamic current pulse and multi-axis vibration excitation are synchronously loaded; s3, applying broadband electromagnetic interference signals and monitoring signal crosstalk amplitude and bit error rate changes; s4, the sensor nodes are dynamically configured according to the topological structure of the wire harness; s5, generating a feature vector; s6, outputting a wire harness performance index; s7, outputting a wire harness fault probability prediction value; s8, obtaining prediction data; s9, triggering a high-speed data recording mode, and storing original sensor data 60 seconds before the fault; and dynamically adjusting the gradient step length and the loading sequence in the environment parameter group. Performance evaluation, fault prediction and service life management can be carried out on the wire harness under complex working conditions, and the reliability and safety of an automobile electrical system are improved.

The invention discloses an intelligent coordination control method and system for power plant equipment based on multi-source heterogeneous data, and belongs to the technical field of intelligent manufacturing and industrial automation, and the method comprises the steps: deploying a multi-mode sensor network in the power plant equipment, collecting the multi-source heterogeneous data in real time, and carrying out the real-time data preprocessing through an edge calculation node; carrying out collaborative modeling on the preprocessed data by adopting a hybrid analysis framework, predicting an equipment state trend, identifying a fault propagation path, positioning a root cause and optimizing a maintenance decision scheme; the equipment failure probability is evaluated through a fault diagnosis result, a grading early warning mechanism is triggered, and a rule base is updated and optimized in combination with a dynamic knowledge base; a three-dimensional model is constructed by using a digital twinning technology to carry out virtual simulation and remote control, and maintenance guidance is carried out through an augmented reality auxiliary technology. According to the method, efficient real-time monitoring and fault prediction are achieved, the fault diagnosis time and the operation and maintenance cost are remarkably reduced by combining the fault tree model and the digital twinning technology, and the equipment operation safety and reliability are improved.

The invention discloses a petroleum pipeline abnormity early warning and monitoring method and system based on a multi-source data fusion attention mechanism and a petroleum pipeline abnormity early warning technology, and aims to realize early-stage tiny leakage abnormity detection of a petroleum pipeline through a multi-source data fusion technology and a deep learning technology. According to the technical key points, historical data of pipeline operation states are collected from sound wave, temperature, negative pressure wave and vibration sensors, and a data set is constructed; obtaining a time sequence segment code for algorithm training; constructing a fusion attention module for algorithm training; on the basis of the obtained multi-source data, an attention Transform model is fused, and an adversarial learning method is adopted for training so as to predict the future operation state of the petroleum pipeline; training a petroleum pipeline early warning model by using Gaussian distribution with learnable scale parameters; the trained multi-source data is fused with an attention Transform model to serve as a petroleum pipeline abnormity early warning model, future operation state abnormal behaviors are recognized, and the failure probability is obtained. According to the method, the multi-source data analysis technology and the deep learning technology are fused, and the innovative Gaussian kernel scaling parameters and the failure probability calculation method are combined, so that the accuracy and reliability of early warning of the abnormity of the petroleum pipeline are effectively improved, and the method particularly has important application value in the aspect of micro leakage detection.

The invention relates to a hydrogen fuel cell system reliability evaluation method based on a Weibull distribution model, and the method comprises the steps: collecting the state detection quantity of a hydrogen fuel cell system in real time, and carrying out the calculation to obtain a wear index and a functional age; historical failure data of the hydrogen fuel cell system are obtained, and shape parameters and scale parameters are obtained through calculation; based on the wear index, the functional age, the shape parameter and the scale parameter, a Weibull distribution reliability model is constructed and used for calculating the cumulative failure probability and the aging failure rate; and determining the failure risk and the residual life of each component in the hydrogen fuel cell system based on the accumulated failure probability and the aging failure rate in combination with the improved risk priority. Compared with the prior art, the method has the advantages that the problems of single parameter evaluation, static analysis and inadequate individuation in the prior art are solved, dynamic, intelligent and systematic reliability evaluation is realized, and scientific support can be provided for full-life-cycle management and maintenance optimization of the hydrogen fuel cell system.

The invention discloses a boiler state monitoring and predicting system based on artificial intelligence, and relates to the technical field of state monitoring. The system comprises a data acquisition part, a combustion stability analysis part, a hydrodynamic instability risk assessment part, a steam drum water level fluctuation prediction part and a fault probability and service life assessment part. The data acquisition part is used for acquiring boiler operation data and water circulation data; the combustion stability evaluation part is used for calculating a thermodynamic efficiency index; the combustion stability analysis part is used for calculating a combustion stability coefficient; the hydrodynamic instability risk assessment part is used for calculating a hydrodynamic instability risk index; the steam drum water level fluctuation prediction part is used for calculating and predicting steam drum water level fluctuation; and the fault probability and service life evaluation part is used for evaluating the fault probability and the residual service life of the boiler. According to the invention, the comprehensiveness, accuracy and predictability of boiler operation state monitoring are obviously improved.

The invention discloses a vehicle frame production information management method and system, and relates to the technical field of production optimization. A vehicle frame production information management system comprises a sensing module, a data storage module, a data processing module, a production optimization model module, a plan management module, a quality inspection management module and a data analysis and decision support module. Based on a machine learning model and a neural network technology, historical operation data and real-time parameters of the equipment are analyzed, and the future fault probability and the fault type of the equipment are accurately predicted; and when the equipment fault risk exceeds the preset threshold value, early warning is triggered, and a maintenance or replacement suggestion is generated, so that the shutdown risk caused by the equipment fault in the production process is remarkably reduced, and intelligent and predictive maintenance of equipment management is realized.

The invention discloses a progress management system and method for electric power engineering, and belongs to the field of electric power construction engineering, and the system comprises a data collection module which is used for obtaining real-time sensor data, construction log data and external data of a construction site; the feature engineering module is used for integrating the multi-source data of the data acquisition module based on a spatial-temporal feature fusion engine and constructing an enhanced feature matrix; the model processing module is used for outputting a progress deviation and an equipment fault probability based on the PhaseAware LSTM network; key features are selected according to the construction stage, structured prediction is carried out based on a random forest, and a progress deviation rate prediction value and a risk factor evaluation result are output; and the dynamic weight fusion module is used for dynamically distributing model weights based on the prediction results of the random forest and the LSTM, the real-time equipment state and the environment parameters, and generating a fused progress prediction value. According to the invention, high-precision progress prediction and risk early warning can be output.

The invention belongs to the technical field of electric power environment monitoring, and particularly relates to an intelligent terminal data acquisition control system and method based on an electric power environment. The intelligent terminal data acquisition control method based on the electric power environment comprises the following steps that S10, an intelligent terminal acquires environment data and operation data in a target object, associates the environment data with the operation data according to acquisition time, generates an associated data set with a timestamp and stores the associated data set to the local; and S20, the intelligent terminal calls the local historical fault data and the associated data set, and an analysis model is constructed based on a multi-factor coupling analysis model. According to the scheme, the problem that the comprehensive assessment accuracy of the overall operation risk of the target object is low due to the fact that the intelligent terminal cannot capture the hidden risk formed by multi-factor coupling is solved through multi-factor coupling analysis, and meanwhile, the fault probability is cooperatively reduced, the environmental adaptability is enhanced, decision-making intelligence is achieved by combining hierarchical response and dynamic self-adaptive adjustment, and the risk assessment efficiency is improved. And full-life-cycle intelligent management of the power equipment is achieved.

The invention discloses an automatic driving test scene set optimization method, apparatus and device, and a storage medium. The method comprises the steps of generating a simulation scene file in an OpenSCENARIO format through preprocessing data; analyzing the simulation scene file through a teacher model, outputting a risk description text, receiving a scene feature vector and the risk description text through a student model, and outputting a failure probability; determining a comprehensive value index according to the failure probability, dynamically updating a scene library of automatic driving test scenes, collecting failure data in an AUT test, performing incremental fine tuning on the student model, and obtaining an optimized target test scene set; the problem of gradient estimation variance explosion caused by sparseness disasters can be effectively solved, the stability of model training is improved, the recognition capability of a rare long-tail failure scene is enhanced, the accuracy of failure probability prediction is improved, the judgment accuracy of the model to a boundary scene is improved, and the accuracy and comprehensiveness of scene value quantification are improved.

The invention relates to the technical field of power system automation, and discloses a power grid rolling scheduling deep reinforcement learning decision-making method in extreme weather, which comprises the following steps: constructing a tower vulnerability model and a lead vulnerability model to calculate the fault probability of each power transmission line; constructing a running state time sequence transfer model of each power transmission line, and generating a high-risk cascading fault scene set through Monte Carlo sampling; constructing a power grid rolling scheduling mathematical optimization model considering the successive failure of the power transmission line caused by the extreme weather; constructing a Markov decision process model, and training and constructing a strategy network; and outputting the active output adjustment amount and the load shedding amount of the generator by using the strategy network, executing the control strategy, and circulating the steps until the end moment of the extreme weather is reached. The method has the advantages that the problem of intra-day rolling scheduling decision making of the power grid in an uncertain time sequence scene is solved by using a deep reinforcement learning technology, and the safe operation and reliable power supply capability of the power grid under the condition of power transmission line time sequence cascading failure caused by extreme weather is effectively improved.

The invention discloses an intelligent control method and system based on the operation state of power equipment, and relates to the technical field of power equipment safety, and the control method comprises the steps of data collection, feature extraction, model establishment, intelligent control, model optimization and maintenance and storage. The method has the advantages that the power equipment is divided into high, medium and low risk levels, and the data acquisition frequency is dynamically adjusted according to the fault probability and the deviation degree of the key operation parameters, so that targeted data monitoring can be realized, the data acquisition efficiency is improved, the equipment state change can be responded in time, and the reliability of the power equipment is improved. The method effectively reduces the risk of fault occurrence, analyzes the feature data through employing the K-means clustering algorithm, can accurately divide the data into a normal data set, an abnormal data set and a fault data set, improves the intelligent degree of data processing, and also provides a high-quality data basis for subsequent model training.

The invention discloses a wind turbine generator running state analysis method and system based on rotating speed monitoring, and relates to the technical field of wind power generation, and the method comprises the steps: collecting wind turbine generator data and historical data of the wind turbine generator data, and carrying out the preprocessing of the collected wind turbine generator data; performing rotation speed monitoring verification based on fault tree analysis, training a machine learning model in combination with historical data, and performing fault diagnosis by using a rotation speed monitoring verification result; and automatically generating a repair scheme based on the fault diagnosis result, executing the repair scheme, and recovering the normal working state of the wind turbine generator. According to the wind turbine generator running state analysis method based on rotating speed monitoring, data modeling is carried out by using the optimal characteristics of the wind turbine generator, the correlation among the characteristics is captured through the multi-dimensional Gaussian model, the fault probability of each component is calculated, the fault diagnosis precision is improved, and the fault diagnosis efficiency is improved. And the limitation of only depending on a single data source in the prior art is overcome.

The invention discloses a multi-axial fatigue and reliability analysis method for a semi-submersible wind turbine supporting structure, which comprises the following steps of: firstly, determining an input load power spectral density function of each part of an offshore semi-submersible wind turbine by combining a motion theory, a wind speed spectrum theory and a wave spectrum theory of the offshore semi-submersible wind turbine; carrying out finite element analysis on the offshore wind turbine in a frequency domain according to the load power spectral density function, determining a dangerous point through a frequency domain method, calculating damage by using a W-B critical plane method, predicting the fatigue life of the dangerous point in a time domain, verifying that wind-induced damage is far greater than wave-induced damage, and finally solving a failure probability curve of the wind turbine support structure. And multi-axial fatigue and reliability analysis of the semi-submersible wind turbine supporting structure is completed. According to the method, the randomness of the marine environment and the calculation interval of the wind turbine load can be well unified, the accuracy of predicting the fatigue life of the wind turbine supporting structure is improved, and the method has universality for any type of wind turbine supporting structures and is easy and convenient to operate and wide in application range.

The invention discloses a bridge network anti-seismic performance rapid evaluation method based on capability spectrum analysis. The method comprises the following steps: firstly, carrying out finite element modeling on bridges in a regional bridge network needing to be analyzed by using OpenSees, extracting piers, and carrying out vulnerability analysis on the piers; a bridge vulnerability curve is obtained based on a capacity spectrum method, then the failure probability of the bridge in a certain damage state serves as a post-earthquake damage index, and the bridge failure probability is replaced by the post-earthquake failure probability of the corresponding road section; after the post-earthquake failure probability of each road section exists, all paths between a starting point and an ending point, namely, to-be-researched OD pairs in a network area are analyzed, the passing probability of each path after the earthquake is calculated based on the Bayesian theorem and an event independence calculation method, all the paths under the OD pairs are sorted according to the passing probability, and therefore the optimal passing path is obtained; and the path with small passing probability is avoided. According to the invention, a scientific basis is provided for regional bridge network anti-seismic planning and emergency management.

The invention relates to the technical field of generator monitoring, in particular to an online state monitoring method and system for a water-turbine generator set, and the method comprises the steps: collecting data, and carrying out the preprocessing of the data through a lightweight TinyML reasoning model; dimension reduction processing is carried out on the preprocessed data through PCA, and dynamic normalization is carried out on the data after dimension reduction; performing convolution feature extraction on the normalized data through a feature extraction module, and extracting attention enhancement features from the convolution features through a sparse attention mechanism; calculating a posterior probability based on Bayesian network topology through a Bayesian network feature fusion module; a fault probability vector and an integrated feature vector are obtained through combination of a multi-modal fusion model and a posterior probability; and through the fault probability vector, predicting residual life and current working condition characteristics, and outputting an early warning level, a fault type and predicted fault time. According to the scheme, the diagnosis efficiency and reliability are improved through multi-modal fusion and Bayesian reasoning.

The invention discloses a material transmission control adjustment method and system based on the Internet of Things, particularly relates to the technical field of material transmission control, and is characterized in that the weight of a sensor is dynamically adjusted based on a Kalman filtering algorithm, and the positioning precision of a material barrel is optimized; identifying and positioning an abnormal risk area and predicting a high-risk time period based on spatial-temporal feature analysis of historical data; optimizing a transmission path and speed by combining a digital twinborn model and a path planning algorithm; dynamically adjusting a resource scheduling strategy and calculating an optimal intervention path according to an abnormal prediction result and a path planning parameter; the conveying state of the material barrel is monitored in real time, and the safety and efficiency of the conveying process are ensured; the fault probability calculation module dynamically evaluates the fault probability based on the real-time transmission state; accurate control and dynamic adjustment in the material conveying process are achieved, and the problems that in a traditional material conveying system, positioning precision is low, abnormal prediction is not timely, and path planning is not optimized are solved.

The invention relates to the technical field of wire performance detection, and discloses a wire high temperature resistance detection method, device and equipment and a storage medium. The method comprises the following steps: arranging micro thermocouple array sensors on the surface, the bending part and the connecting point of an outer insulating layer of a to-be-measured wire rod to obtain a wire rod temperature monitoring network; placing the to-be-tested wire rod in a constant-temperature box to execute a high-temperature cyclic loading test, and collecting real-time temperature response data of the to-be-tested wire rod; carrying out temperature gradient analysis and heat distribution characteristic calculation to obtain a temperature change characteristic parameter set; executing temperature fluctuation compensation and thermal stress accumulation calculation to obtain thermal stress characteristic parameters; and carrying out aging characteristic analysis on the wire insulating material through the thermal stress attenuation network model to obtain wire predicted life and failure probability distribution of the to-be-tested wire in a high-temperature environment. The method can comprehensively capture the temperature change characteristics of each part of the wire rod, and achieves the precise prediction of the failure probability of the wire rod under different temperature conditions.

The invention discloses a submarine cable detection method and device and a nonvolatile storage medium. The method comprises the following steps: acquiring the position of a fault point under the condition that a submarine cable is detected to have a fault; determining a first target search range corresponding to the position of the fault point based on the fault positioning response time, the submarine cable routing information processing time and the ocean current speed; determining a fault probability of each routing section of the submarine cable in the first target search range based on the submarine cable depth data, the water depth data and the environment data in the first target search range; and sorting the fault probabilities of the routing segments to obtain a routing segment sequence, and scheduling the patrol ship to perform fault detection based on the routing segment sequence. According to the method and the device, the technical problems of low response speed and low processing efficiency caused by inaccurate positioning and large range of the fault range propagated by the fault point when the submarine cable is identified to have a fault when the submarine cable is monitored in the related technology are solved.

The invention provides a production line equipment fault prediction method and system, and relates to the technical field of fault prediction.The method comprises the steps that multi-source time sequence signals are synchronously collected, a multi-dimensional health feature sequence is extracted, the comprehensive sensing capacity covering multiple states of equipment is established, and further, the fault prediction efficiency is improved. By selecting a key health feature sequence for each device and performing linear fitting to quantify the performance degradation severity, accurate description and early recognition of the performance degradation trend of the device are realized, and in addition, the causal influence intensity between the devices is quantified by adopting a transfer entropy algorithm, so that the accuracy of the performance degradation trend is improved. Scientific modeling and visual analysis of a fault propagation path and a linkage effect in a production line are realized, and a comprehensive risk index is generated through weighted fusion of performance degradation severity and a global impact factor, so that a comprehensive risk assessment index is formed. Based on the cumulative failure probability function, the comprehensive risk index is converted into a fault probability curve changing along with time, a visual mathematical expression of the risk trend is formed, and a scientific basis is provided for decision making.