263 results about "Optimal maintenance" patented technology

The invention relates to the technical field of industrial Internet of Things, in particular to a predictive maintenance method for intelligent factory Internet of Things equipment, which comprises the following steps of: acquiring equipment operation parameters, environment monitoring data and historical maintenance records, constructing a multi-dimensional feature data set, extracting equipment degradation features by adopting a topological graph attention mechanism and a Bayesian network, and establishing a multi-dimensional feature data set; the method realizes equipment health state modeling and fault probability prediction, combines a dynamic adjacency matrix and a multi-objective optimization algorithm, comprehensively optimizes maintenance cost, equipment fault risk and associated equipment influence, dynamically generates an optimal maintenance plan, carries out constraint optimization based on a mixed integer programming method, automatically generates a maintenance instruction sequence, and achieves the optimal maintenance of the equipment. Tasks are issued through the computerized maintenance management system, the PLC control system and the industrial Internet of Things gateway, and the execution state is monitored and maintained in real time. The intelligent level of equipment maintenance is effectively improved, non-planned shutdown is reduced, and the equipment reliability and the production efficiency are improved.

The invention relates to the technical field of bridge structure health monitoring and maintenance, and discloses a bridge structure anti-fatigue performance predictive maintenance method based on digital twinning, and the method comprises the steps: constructing a bridge structure digital twinning model, and deploying a multi-mode sensing network; establishing a bridge fatigue mechanism knowledge graph, and structuring expert experience and historical cases; multi-source heterogeneous evidence fusion is realized based on a DS evidence theory, and evidence conflicts are eliminated; constructing a cause and effect inference engine based on a Bayesian network, and identifying a fatigue mechanism path; and a self-adaptive predictive maintenance decision-making system is realized, and an optimal maintenance strategy is generated. The problems that a traditional bridge fatigue analysis method is difficult to process multi-source heterogeneous data, the fatigue prediction precision is insufficient and the like are solved, and high-precision prediction and precise maintenance of the anti-fatigue performance of the bridge structure are achieved.

The invention relates to an intelligent maintenance decision method and system based on a knowledge graph, and relates to the technical field of intelligent operation and maintenance and decision support and information, and the method comprises the steps: obtaining the operation state data and historical maintenance data of industrial equipment; constructing an industrial knowledge graph and generating an incidence relation model; an optimal maintenance path is obtained based on the analysis of the relationship between the fault type and the maintenance path; optimizing a maintenance task allocation scheme through the optimal maintenance path, the incidence relation model and the maintenance resource constraint condition; carrying out maintenance time window dynamic adjustment analysis to obtain an optimal maintenance strategy; and finally, performing multi-objective optimization iterative analysis to obtain a final maintenance decision. According to the scheme, various factors can be comprehensively considered, and the scientificity and the accuracy of maintenance decision making of the industrial equipment are improved.

The invention provides a fan full life cycle archive management system and a management method thereof, and the system comprises an Internet of Things data collection module which is installed on a key part of a wind turbine generator set and collects vibration, temperature and stress data; the full-life-cycle digital archive management module is used for recording full-process information of the fan from design to operation and maintenance based on a cloud database and a block chain technology, and ensuring data integrity and traceability; the AI predictive maintenance analysis module analyzes data and predicts faults by using deep learning and machine learning algorithms; the intelligent scheduling and maintenance decision support module is used for generating an optimal maintenance plan in combination with external factors; and the responsibility tracing and process optimization module is used for recording maintenance details to optimize the process and spare part management. The management method comprises the steps of data acquisition, storage, analysis, maintenance plan making, task execution and recording, effect evaluation, process optimization and the like. Through cooperative work of all the modules and implementation of the system method, accurate and efficient full-life-cycle management of the draught fan is achieved.

The invention relates to a digital twinning-based power equipment predictive maintenance method, which comprises the following steps of: acquiring time sequence operation data of each power equipment in a power system in real time, and preprocessing the time sequence operation data; based on the preprocessed time sequence operation data, updating the constructed digital twinborn model integrated with the LSTM in real time to obtain a latest digital twinborn model synchronously operating with the physical entity of the power equipment; based on the time sequence operation data, an isolated forest algorithm and a newest digital twinborn model are adopted to carry out fault prediction, and a fault prediction result is obtained; and determining a plurality of maintenance schemes based on the fault prediction result, and evaluating each maintenance scheme to obtain an optimal maintenance scheme and complete a predictive maintenance process. Compared with the prior art, the method has the advantages of improving the reliability and safety of the power equipment and the like.

The invention discloses a die life prediction and maintenance decision system based on digital twinning. The system comprises a data acquisition module, a data fusion module, a digital twinning body construction and updating module, a residual life prediction module, a maintenance decision and optimization module and a closed-loop execution and feedback module. The system collects working condition data and production parameters of a physical mold in real time, generates a comprehensive health state index after fusion processing, constructs a dynamic digital twin, simulates a future production plan based on the digital twin, predicts the remaining service life of the mold, and combines a production schedule, a resource inventory and a cost model. And generating, issuing and executing an optimal maintenance decision scheme. The system continuously updates and optimizes the digital twin by using the maintained result data through a closed-loop feedback mechanism, so that the health state evaluation, the life prediction and the dynamic optimization of the maintenance strategy of the mold are realized, the mold management level is effectively improved, and the maintenance cost and the production shutdown risk are reduced.

The invention provides an engine oil burning diagnosis and maintenance system, and relates to engine oil burning diagnosis. The method comprises a multi-source data acquisition model, a heterogeneous data deep fusion module, a multi-level diagnosis module and a prediction maintenance module. The multi-source data acquisition model is used for acquiring multi-source data in the operation process of an engine in real time through an integrated sensor array, and the multi-source data comprises vibration data, acoustic data, exhaust component data and temperature distribution data; the heterogeneous data deep fusion module is used for performing data conversion on the multi-source data to obtain unified multi-source feature representation; the multi-level diagnosis module is used for performing multi-level diagnosis on the multi-source feature representation to obtain a multi-level diagnosis result; and the prediction maintenance module is used for carrying out maintenance analysis according to the multi-layer diagnosis result to obtain a maintenance decision suggestion containing a predicted life value, a fault evolution path and optimal maintenance time. By means of the scheme, the comprehensiveness and accuracy of engine oil burning diagnosis and maintenance are improved.

The invention relates to the technical field of industrial equipment predictive maintenance and asset management, in particular to a machine vision equipment operation and maintenance cost analysis intelligent management method, which comprises the following steps of: acquiring equipment operation state, external environment and historical operation and maintenance work order data through a sensor group and an equipment log interface, and fusing and removing redundancy to form a multi-source data stream; static and dynamic features are extracted by using a pre-trained health state evaluation model and fused by means of an attention mechanism, and a real-time health state index in a 0-1 interval is output; constructing a dynamic cost prediction model, taking health related parameters, spare parts, manpower and depreciation cost as input, and predicting expected operation and maintenance cost of a specific time window in the future; establishing an optimization decision model by taking minimization of the total operation and maintenance cost and maximization of the equipment availability rate as double targets, and generating an optimal maintenance, spare part purchasing and scheduling scheme; and executing the scheme and acquiring actual data, comparing the actual data with a predicted value for feedback, and iteratively optimizing the core model. The operation and maintenance management accuracy and economy are improved, and the method is suitable for intelligent operation and maintenance of the machine vision equipment.

The invention discloses automatic complete equipment for intelligent predictive maintenance of a primary and secondary fusion circuit breaker. The automatic complete equipment comprises a multi-sensor fusion unit, an edge calculation and analysis module; a parameter interaction module; a predictive maintenance decision unit; the primary and secondary converged communication architecture is used for managing control information and state information on the basis of an IEC61850 (International Electrotechnical Commission 61850) standard; wherein a noise covariance matrix and a feature weight coefficient of the adaptive Kalman filtering health assessment algorithm are dynamically adjusted according to a data quality index and prediction error feedback, and input features of the residual life prediction algorithm based on the LSTM comprise a health index, a change rate and component-level health state information from the health assessment algorithm. Accurate evaluation of the health state of the circuit breaker and accurate prediction of the residual life are achieved, the optimal maintenance strategy is generated, the operation reliability of the circuit breaker is improved, and the maintenance cost is reduced.

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 equipment prediction maintenance framework based on probability residual life, and belongs to the technical field of fault prediction and health management (PHM). According to the framework, through combining a Bayesian neural network and a reinforcement learning technology, probability prediction and dynamic maintenance decision optimization of the residual life of equipment are realized. The method specifically comprises the following steps: acquiring sensor data in equipment operation, and preprocessing to generate a training data set; constructing a Bayesian neural network (BNN), utilizing variation reasoning to approximate posteriori distribution, and outputting probability distribution of residual life through Monte Carlo sampling; based on a probability prediction result, constructing a reinforcement learning environment model, and defining a state space containing residual life distribution, a spare part state and a maintenance action and a reward function; a Double DQN algorithm is adopted to optimize a maintenance strategy, and intelligent decision-making of the optimal maintenance time and the optimal ordering time of equipment is dynamically realized through an epsilon-greedy algorithm, so that the maintenance cost and the fault risk are minimized. According to the method, probability residual life distribution and reinforcement learning decision are innovatively combined, the problem that a traditional point estimation model ignores uncertainty is solved, an intelligent maintenance framework is constructed through a reinforcement learning method, and dynamic optimization of maintenance decision is achieved. The NASA aero-engine data set verification shows that compared with a traditional method, the optimization effects of prediction errors, uncertainty quantification, the maintenance cost rate and the like are remarkable, and the reliability and economical efficiency of equipment are effectively balanced.

The invention discloses a ship equipment maintenance support system and method based on artificial intelligence, and the system collects the operation data of equipment in real time through a data collection and integration module, and digitalizes and integrates the historical maintenance data into a unified database; a fault diagnosis model is constructed through the model diagnosis module to carry out fault diagnosis and output a diagnosis result; an optimal maintenance scheme is provided through a maintenance decision support system according to a fault diagnosis result in combination with multi-dimensional data; real-time monitoring and trend analysis are carried out on operation data of the equipment through the fault prediction module, potential faults of the equipment are predicted, and a maintenance plan is made in advance. According to the invention, equipment operation data is collected in real time through a sensor, accurate fault diagnosis and prediction are realized in combination with a deep learning algorithm, an optimal maintenance scheme is formulated in combination with multi-dimensional data, and a maintenance plan is dynamically adjusted.

The invention discloses a rural historical building protection evaluation system based on big data, and relates to the technical field of building protection. Comprising the following steps: a data processing module normalizes environmental data to generate an environmental degradation factor, extracts a crack depth parameter and a facade weathering parameter from structure detection data through a feature extraction algorithm, and generates a building life attenuation curve through correlation analysis; and the decision module establishes a maintenance optimization model according to the building life attenuation curve and historical maintenance data, generates a risk level through threshold comparison, evaluates expected effects of different maintenance modes on degradation delay, and outputs an optimal maintenance suggestion. According to the method, accurate prediction of the rural historical building degradation process and optimal recommendation of the maintenance strategy are realized, and the scientificity and efficiency of protection work are improved.

The invention provides an equipment fault prediction and maintenance method and system based on artificial intelligence, and the method comprises the steps: collecting the multi-modal data of equipment; multi-modal features of the multi-modal data are extracted by means of MS-TCN; the multi-modal features are input into AMENN, and a fault prediction result is obtained; according to a fault prediction result, utilizing a multi-target deep reinforcement learning algorithm to generate an optimal maintenance strategy and performing maintenance; and comparing an actual maintenance result with a prediction result, and optimizing a feature processing process and a fault prediction process. According to the equipment fault prediction and maintenance method and system based on artificial intelligence, the MS-TCN is introduced to obtain the multi-modal features of the multi-modal data, and the AMENN is introduced to carry out fault prediction; mODRL is introduced to generate an optimal maintenance strategy corresponding to a fault prediction result, and a fault feature discrimination process is optimized based on SSCL, so that the suitability of prediction and maintenance is greatly improved.

The invention discloses a municipal facility maintenance strategy optimization method and system based on big data driving, and relates to the technical field of urban operation and maintenance management and big data decision support, and the method comprises the steps: carrying out the data cleaning and standardization processing of multi-source operation state data, and forming a facility state database; the method comprises the following steps: extracting facility degradation trend characteristics, establishing a facility state degradation prediction model, calculating a comprehensive health degree index, establishing a multi-objective optimization model based on prediction of a degradation trend, solving an optimal maintenance opportunity and a resource allocation scheme, and generating a maintenance strategy list. And comparing and analyzing the maintenance strategy list with historical maintenance effect data in the facility state database to obtain strategy optimization feedback parameters, and dynamically updating parameter setting of the facility state degradation prediction model. According to the invention, the management mode from passive fault repair to active preventive maintenance is changed.

The invention relates to the technical field of equipment fault diagnosis, and discloses an intelligent fault detection method for a speed reducer, and the method comprises the steps: extracting fault feature knowledge from a source domain based on a domain adaptive transfer learning algorithm, migrating the fault feature knowledge to a target domain, and generating a synthetic fault sample based on physical model constraints; constructing interaction influence among the multi-subject causal capture equipment; executing causal intervention and anti-factual reasoning, and determining a fault root cause; constructing a fault knowledge graph and continuously optimizing the fault knowledge graph; and constructing a preventive maintenance decision system to generate an optimal maintenance strategy. According to the method, high-accuracy fault diagnosis can be realized under the condition of sample scarcity, interaction influence among multiple devices is analyzed, fault root causes are traced, and reliable preventive maintenance decision support is provided.

The invention discloses a factory equipment predictive maintenance scheme optimization method based on reinforcement learning, and relates to the technical field of equipment maintenance scheme prediction, and the method comprises the steps: carrying out the fusion through employing a space-time attention mechanism based on an equipment health index, equipment operation duration and a current load level, generating an equipment state vector, constructing a reinforcement learning framework, and carrying out the optimization of a factory equipment predictive maintenance scheme. A multi-target reward function is embedded in the reinforcement learning framework, a PPO algorithm is adopted for optimization, a reinforcement learning model is generated, the reinforcement learning model is trained by using the equipment state vector, the trained reinforcement learning model is obtained, the trained reinforcement learning model is deployed to an actual industrial control unit, the latest equipment state vector is received in real time, and the final equipment state vector is obtained. And obtaining an optimal maintenance strategy. And converting the optimal maintenance strategy into a specific maintenance instruction and executing the specific maintenance instruction. Through the factory equipment predictive maintenance method, rapid response to emergencies is facilitated, greater loss is avoided, resource allocation is optimized, and enterprise competitiveness is enhanced.

A predictive maintenance method based on an elevator operation and maintenance time sequence knowledge graph comprises the steps that firstly, the elevator operation and maintenance time sequence knowledge graph is constructed, predictive maintenance of electromechanical equipment parts is achieved based on elevator part maintenance period optimization, a theoretical distribution model based on Weibull distribution is constructed, and parameters of Weibull distribution are fitted through a least square method. Therefore, the model can accurately reflect the failure rule of the elevator parts; calculating an inference result of the elevator operation and maintenance time sequence knowledge graph through a graph convolution model and a Horkes process model, and carrying out recursive relation calculation of component fault rate functions in N maintenance cycles; an elevator component maintenance comprehensive cost simulation model is constructed, and the optimal maintenance cycle of elevator components is calculated through a continuous time Markov chain method; the fault prediction accuracy is improved.

The invention provides a bridge group multi-target maintenance decision-making method fusing an evolutionary algorithm and artificial intelligence, and relates to the technical field of civil engineering and artificial intelligence crossing. The method comprises the steps of defining bridge group maintenance cost and structure failure risks, representing preferences of decision makers for different decision targets by weight combinations, and constructing a multi-target maintenance decision optimization model; encoding the weight combination into an individual of a multi-objective evolutionary algorithm, and randomly generating an initial population; aiming at each generation of weight combination, constructing a bridge group Markov decision-making environment; learning an optimal maintenance strategy by adopting an A2C training reinforcement learning agent; the optimal maintenance strategy is evaluated, and an evaluation result is used as individual fitness to be fed back to the multi-objective evolutionary algorithm; using a multi-objective evolutionary algorithm to perform evolutionary search on the multi-objective weight combination; and through a closed-loop feedback mechanism, outputting a Pareto optimal solution set containing an optimal maintenance strategy under various weight combinations, thereby realizing collaborative optimization of weight optimization and strategy learning.

The invention provides a cost-oriented civil aircraft fleet planned maintenance scheme optimization method, relates to the field of regular maintenance plan and cost optimization of an aircraft fleet, and aims to improve the maintenance efficiency of the aircraft fleet and reduce resource waste in task intervals. According to the method, the optimal maintenance frequency and interval are realized by integrating the regular inspection plans and combining the balanced maintenance strategy. According to the method, a discrete event simulation model is adopted, a fleet maintenance plan is subjected to multi-dimensional simulation and optimization, and an efficient maintenance task allocation scheme is formulated according to constraint conditions such as fleet scale, flight hours, task types and hangar resources. Through optimization of a maintenance task distribution mode and fine adjustment of task intervals, the maintenance cost of a fleet can be effectively reduced, and the airworthiness and the utilization rate of aircrafts are improved. The method provides a scientific basis for an airline company to make a maintenance decision, and has an important application value for optimizing the operation and maintenance cost of an aviation fleet.

The invention discloses a bridge service intelligent evaluation and early warning method and system based on dynamic and static load-multi-modal data fusion, and the method comprises the steps: S1, installing a vibration acceleration sensor and a strain gauge at a bridge control part, and collecting data; locally preprocessing data by adopting an edge computing framework, extracting feature values, encrypting and transmitting the feature values to a cloud; identifying and collecting diseases, and combining a deep learning network model to realize pixel-level crack segmentation; s2, establishing a space-time-measuring point-disease three-dimensional correlation model; s3, a three-level early warning threshold system is set, different early warning levels have different requirements for vibration amplitude and crack length indexes, and when a monitoring index exceeds a threshold value, early warning information is pushed to a terminal in real time through 5G; S4, a decision engine is constructed based on a Q-Learning algorithm or a deep reinforcement learning model, early warning levels, residual life and maintenance cost parameters are input, and the early warning level, the residual life and the maintenance cost parameters are calculated. And outputting the optimal maintenance scheme.

The invention discloses a power failure event cooperative processing method, system and device based on geographic information and a medium, and relates to the technical field of power system power distribution network fault processing and informatization. The method comprises the following steps: when a power failure event occurs, acquiring multi-source heterogeneous data from an associated system; performing fault analysis on the multi-source heterogeneous data through a fault diagnosis model, and outputting fault positioning information and a fault type; obtaining associated maintenance resource state data and real-time traffic road condition data according to the fault positioning information; the maintenance resource state data comprises a rush repair team position, a skill level and a vehicle state; and based on the fault positioning information, the fault type, the user attribute, the historical work order data, the maintenance resource state data and the real-time traffic road condition data, generating an optimal maintenance scheduling scheme through a reinforcement learning scheduling model. The cross-department and cross-business cooperative processing is realized, and the first-aid repair efficiency and the resource utilization rate are remarkably improved.

The invention discloses an intelligent scheduling management system and method for a power grid communication maintenance plan, and belongs to the field of intelligent scheduling management, and the method comprises the steps: collecting the historical operation data of power grid communication, and carrying out the preprocessing, and obtaining a sample data set; performing feature screening on the sample data set based on an LASSO regression model to obtain key features; constructing a prediction model by using the screened key features, and training and evaluating the prediction model by using the divided training sample set and verification sample set to obtain a fault prediction model; inputting the real-time operation data into the fault prediction model, obtaining the prediction probability of the equipment fault, and comparing the prediction probability with a preset probability threshold to judge whether the equipment has a fault or not; the method comprises the following steps: establishing a maintenance plan scheduling optimization model by setting an optimization target and a constraint condition, and solving by adopting a heuristic algorithm to generate an optimal maintenance plan scheduling scheme; and outputting the optimal maintenance plan scheduling scheme to the system, and executing the optimal maintenance plan scheduling scheme after the optimal maintenance plan scheduling scheme is audited and adjusted by a manager.

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 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 discloses a mixed reality mine ventilation equipment maintenance guidance method. The method comprises the following steps: S1, collecting real-time state data of mine ventilation equipment based on a multi-source sensor; s2, collecting historical maintenance data, performing natural language preprocessing on the historical maintenance data, constructing an entity-relation-attribute triple for the preprocessed data through a BERT model, and obtaining an optimized maintenance knowledge graph based on the entity-relation-attribute triple; s3, taking the maintenance process of the mine equipment as a Markov decision process, and taking the optimized maintenance knowledge graph as a state space to carry out Markov decision process modeling to obtain an MDP prediction model; and S4, based on the MDP prediction model, constructing intelligent agent learning through improved deep reinforcement learning, selecting an optimal maintenance action strategy for different equipment states under the MDP prediction model, and outputting an optimal maintenance guidance strategy.

The invention relates to the technical field of intelligent electromechanical equipment management, and discloses an intelligent electromechanical equipment maintenance reminding system which comprises a real-time data acquisition module, an equipment health state estimation module, an optimal control module, a robust optimization module, a multi-target optimization module and an execution and feedback module. The method comprises the following steps: acquiring running state data of equipment in real time through a sensor; estimating a current health state of the equipment and predicting a future trend; calculating an optimal maintenance period by adopting an optimal control algorithm based on an estimation result of the equipment health state; in the equipment operation process, disturbance and uncertainty are considered, and robust optimization adjustment is carried out on the maintenance period; and comprehensively considering a plurality of optimization targets. According to the invention, the operation state data of the equipment is collected in real time through the sensor, and the multi-objective optimization method is combined to balance the health maintenance, the fault risk and the maintenance cost, so that the technical effect of dynamically optimizing the maintenance period is achieved.

The present invention relates to the technical field of avionics equipment, and disclosed is a cross-domain maintenance decision-making method for coordinated optimization of maintenance service outlets, activities, and resources. The method comprises the steps of: on the basis of a fault phenomenon, acquiring a set of a variety of maintenance activities; determining whether resources required for a faulty product are missing at a current service outlet, and if the resources are missing, separately computing a resource scheduling time and cost required for each maintenance activity for which resources are missing; computing a fault repair time, repair costs, and repair probability of a certain maintenance activity; and determining a goal of a maintenance decision, and outputting an optimal maintenance scheme. The present invention can assist technicians in making maintenance decisions during maintenance of electronic products, reduce the waste of time and resources caused by the inconsistencies and uncertainties of maintenance activities, guide maintenance activities of remote operation and maintenance, reduce maintenance time, and reduce operation and maintenance costs.

The invention discloses an intelligent fire-fighting equipment fault prediction method, and belongs to the technical field of intelligent maintenance of fire-fighting equipment. According to the method, pressure, electrical waveform and environmental data are dynamically acquired through a multi-source sensor, spatial-temporal features are extracted in parallel by adopting a bidirectional LSTM and a multi-scale convolutional network, and feature fusion is realized by utilizing a cross-modal attention mechanism; outputting a fault probability based on a gradient boosting decision tree model, and predicting the remaining service life in combination with Bayesian regression; the firmware upgrading authority is controlled through a health degree dynamic scoring system, and the upgrading safety is guaranteed through AES-256-CBC encryption and SM3 signature verification; and generating an optimal maintenance path based on an improved genetic algorithm, and scheduling an engine to respond to a sudden fault in real time. Tests show that the fault early warning accuracy rate reaches 93.1%, the maintenance cost is reduced by 45%, the upgrade failure blocking rate is 100%, and the intelligent level and reliability of operation and maintenance of the fire-fighting equipment are remarkably improved.

The invention relates to the technical field of offshore wind turbine generator operation and maintenance, and discloses a failure evaluation system based on an offshore wind turbine generator. Comprising a sensor data acquisition module, a data preprocessing module, a failure feature extraction unit, a failure probability calculation module, a maintenance decision module and the like. The system obtains parameters such as vibration and temperature of the wind turbine generator in real time, after preprocessing, failure features are extracted through a deep convolutional neural network, and a dynamic Bayesian network model is constructed to calculate the component failure probability. An optimal maintenance path is generated by using an improved ant colony optimization algorithm, and meanwhile, a dynamic risk assessment module, an abnormal signal detection module and a maintenance strategy optimization module are arranged. The method can accurately evaluate the unit failure risk, timely discover abnormity, optimize the maintenance decision, improve the operation reliability of the offshore wind turbine generator, reduce the operation and maintenance cost, and guarantee the stability and high efficiency of offshore wind power generation.