422 results about "Preventive maintenance" patented technology

The present invention relates to the technical field of power system equipment and control. The method of the present invention comprises: installing sensors to acquire data for online real-time monitoring, and constructing a comprehensive condition online monitoring model for comprehensive data analysis; comprehensively evaluating the health condition of a generator on the basis of a comprehensive analysis result, and identifying fault causes; and constructing a generator comprehensive data intelligent monitoring and dynamic early warning model to perform insulation degradation trend analysis and prediction on the generator. In the present invention, by comprehensively monitoring the condition of a turbine generator, various key indicators of the generator are captured in real time, and trends and patterns underlying the data are revealed, providing technical support for accurately evaluating the health condition of the generator; and continuous monitoring for the insulation condition of the generator allows for proactive identification of potential risks, thereby providing decision-making support for preventive maintenance, helping operators take measures promptly, preventing faults, improving the reliability and safety of the generator.

The invention relates to the technical field of hoisting equipment monitoring, and discloses a hoisting equipment digital real-time monitoring system based on the Internet of Things. A dynamic load analysis module of the system collects multi-dimensional operation parameters in real time through distributed edge computing nodes; the risk situation assessment module executes tensor decomposition operation on the parameters, extracts feature vectors and generates a three-dimensional risk map; the self-adaptive safety control module dynamically adjusts the working state of the equipment according to the risk map; the digital twin mapping module is used for realizing time-space synchronous mapping of real-time parameters and a three-dimensional model and outputting holographic running state projection; and the cloud collaborative diagnosis module fuses the historical fault case library to generate a preventive maintenance strategy and returns the preventive maintenance strategy. The system can comprehensively monitor the equipment state, accurately assess the risk, realize dynamic safety control and preventive maintenance, and improve the safety and reliability of the operation of the hoisting equipment.

The invention belongs to the technical field of fault early warning, and discloses a heater fault early warning method and system based on current analysis. Comprising the steps that transient current signals of all heating wire loops in the copper bush heater are collected in real time, harmonic characteristic parameters are extracted, and a dynamic current fingerprint database and a multi-dimensional harmonic characteristic spectrum are generated respectively; dynamically calculating the contact impedance between each heating wire and the copper sleeve groove according to the dynamic current fingerprint database and the multi-dimensional harmonic characteristic spectrum; the current distribution path of each heating wire in the copper sleeve groove is restored in real time, the abnormal contact form of each heating wire is identified, the falling risk probability and falling risk strength of each heating wire are dynamically predicted by fusing the falling precursor signals of each heating wire detected in real time, and a grading early warning strategy report is generated in real time; according to the invention, fault early warning can be converted into preventive maintenance from post-maintenance, the intelligent level of operation and maintenance of the heater is improved, and the continuity and safety of production are guaranteed.

The invention belongs to the technical field of vehicle fault prediction, and discloses a vehicle fault evolution law modeling method and system based on digital twinning. According to the method, vehicle running state data are obtained and mapped to a digital twin virtual state space, a fault feature evolution sequence is extracted in combination with historical fault records, a state transition topological graph is constructed, the spatial distance between a real-time state and a feature vector group is calculated to match an evolution path, and the state transition topological graph is obtained. And a fault evolution prediction result is determined based on the corresponding timestamp information, and a preventive maintenance strategy is generated. According to the invention, accurate modeling of the vehicle fault evolution rule is realized, and the fault prediction accuracy and the maintenance efficiency are 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 discloses a multi-element coupling heat supply monitoring system based on a meteorological condition prediction model, belongs to an industrial control system, realizes deep coupling of meteorological data and an equipment operation state, and solves a fusion error problem caused by fixed meteorological element weight in traditional monitoring to a certain extent. By establishing the efficiency evaluation model containing the environment compensation mechanism, the interference of external temperature fluctuation on equipment performance evaluation is eliminated, and the accuracy of the operation efficiency index is improved. A multi-dimensional health assessment system is adopted to integrate equipment maintenance data and real-time operation parameters, potential fault risks can be recognized in advance, and data support is provided for equipment preventive maintenance.

The invention belongs to the field of data processing, and provides an equipment comfort measurement fault identification method, system and equipment, and the method comprises the steps: obtaining a plurality of modal sequences which are consistent with a sampling signal segment in length and are aligned in bit sequence through variational modal decomposition; traversing each sampling signal segment according to a bit sequence to form a signal segment extended wave; extracting the peak value of the signal segment extended wave and the characteristic of the bit sequence of the peak value to generate an extended wave ratio sequence; the difference between the wave ratio sequence of each sampling signal segment and the original sequence is used as the wave ratio fall; and screening each sampling signal segment according to the wave ratio fall by using the median of the wave ratio fall of all the sampling signal segments, and identifying a signal comfort abnormal segment. The method is not sensitive to amplitude fluctuation of random noise, but is more sensitive to envelope impact and bit sequence displacement of a specific frequency band, can prompt slight problems such as guide shoe clearance, guide rail joint impact and abnormal transmission of a traction machine in the early stage, and is beneficial to preventive maintenance.

The invention provides an internal damage assessment method and system based on a multi-modal graphite component, and relates to the technical field of material deterioration assessment, and the method comprises the steps: generating a standardized multi-modal data stream through synchronously collecting five-modal data; extracting acoustic and electro-thermal physical features from the multi-modal data stream, and constructing a multi-dimensional damage sensitive feature vector; inputting the multi-dimensional damage sensitive feature vectors into a deep learning network corrected by a physical constraint layer, and extracting crack quantization parameters from the deep learning network; inputting the crack quantization parameters into a digital twin driven irradiation-creep-fatigue coupling damage model, and calculating the residual life and failure probability of the overall structure; and in combination with the space coordinate information of the three-dimensional crack probability field, a preventive maintenance instruction and schedule for the high-risk modular component are generated and optimized. According to the method, accurate perception, quantitative evaluation, accurate prognosis and optimized decision making of the internal damage of the graphite component are realized, the resource utilization efficiency is maximized, and unnecessary shutdown loss is reduced.

The invention belongs to the technical field of wind turbine generator maintenance, and particularly relates to an offshore wind turbine generator two-stage rolling maintenance method considering subsystem correlation, which comprises the following steps: S1, constructing a comprehensive reliability model; s2, taking the minimization of the total maintenance cost in the operation cycle of the wind turbine generator as a target, and forming a target optimization problem based on the comprehensive reliability model; solving the target optimization problem to form a first-stage long-term maintenance strategy; s3, when an operation window meeting the storm accessibility condition appears, determining a subsystem of which the current reliability is lower than a preventive maintenance threshold value; s4, the opportunity maintenance benefit ratio is calculated, and if the opportunity maintenance benefit ratio is larger than a processing threshold value, advanced maintenance of the subsystem is triggered; and S5, after the advanced maintenance is executed each time, the optimal preventive maintenance time point is solved again so as to dynamically update the subsequent maintenance plan. According to the method, the fault coupling and degradation evolution law between subsystems can be accurately described, and the storm accessibility window is actively utilized to dynamically adjust the maintenance plan.

The invention discloses an intelligent cable life prediction method and system based on data driving and a cable, and relates to the technical field of intelligent operation and maintenance of power equipment, and the method comprises the steps: collecting the current, voltage, partial discharge, temperature, environment temperature and humidity and other multi-dimensional historical operation time sequence data of the cable; a cable comprehensive state feature sequence is generated through processing of a multi-source heterogeneous data fusion technology; extracting a load fatigue cumulant, an insulation degradation index and a thermal stress damage degree based on an aging mechanism to form a degradation feature vector; inputting the vector into a pre-training model for time sequence evolution deduction, and outputting probability distribution of residual life and a key failure time node; and generating a preventive maintenance decision scheme including a maintenance window, an operation priority and inventory early warning. According to the method, more accurate evaluation and risk quantification of the cable life are realized through mechanism characteristic driving and probabilistic prediction, and a direct decision basis is provided for active operation and maintenance.

The invention provides a power distribution communication network fault management process optimization method, which is based on a fault evolution reverse deduction technology of historical repair knowledge, and realizes efficient fault root cause positioning and repair recommendation by combining a dynamic space-time atlas and a graph database technology. The method comprises the following steps: constructing a dynamic space-time atlas, and storing power distribution communication network topology and fault data; constructing a fault causal relationship model, and describing a fault state transition probability and an evolution causal chain; multi-path hypothesis testing is executed, possible fault sources and evolution paths are generated, and confidence scores are distributed; similar fault modes and repair measures are retrieved from a historical repair knowledge base, and a directional repair strategy is recommended; and space-time backtracking analysis is realized, and the whole fault evolution process is visually displayed. According to the method, the root cause positioning efficiency and accuracy are remarkably improved, the fault processing flow is improved, the fault prediction capability is improved, the preventive maintenance level is enhanced, and continuous accumulation and optimized application of maintenance knowledge are realized.

The invention relates to a power transmission line fault risk grading evaluation method based on multistage early warning, and belongs to the field of power system safety. Comprising the following steps: acquiring an insulator surface pollutant data generation matrix; extracting spatial distribution characteristics of pollutants; leakage current waveforms under different humidity are calculated; extracting a leakage current harmonic component and pulse characteristics; dividing pollution flashover risk grades; matching the maintenance strategy and generating an equipment scheduling instruction; and performing fault risk grading evaluation to generate a report. According to the method, through multi-source data fusion and dynamic modeling, accurate evaluation and preventive maintenance of the pollution flashover risk of the insulator are realized, and the safety and reliability of a power system are improved.

The invention provides a preventive maintenance method, which comprises the following steps of: fusing data of a plurality of data sources to obtain fused data; performing feature extraction on the fused data to obtain feature data of at least one dimension; evaluating a target system and target equipment in the target system based on the feature data of at least one dimension to obtain multi-level health degree evaluation information; and generating preventive maintenance information based on the multi-level health degree assessment information. The invention further provides electronic equipment, a readable medium and a program product.

The invention discloses a laboratory detection equipment intelligent management system based on edge calculation and reinforcement learning, and relates to the technical field of equipment intelligent management, the system comprises a multi-dimensional data acquisition module, a model training and feature library construction module, a deviation degree research and judgment module, a model optimization module and an interaction and execution module; according to the invention, through integration of the multi-dimensional data acquisition module and the model training and feature library construction module, all-directional precision monitoring of the operation state of laboratory detection equipment is realized, and various feature data in the operation process of the equipment can be captured in real time through the multi-dimensional sensor array; the model training and feature library construction module uses a deep learning model of a CNN-LSTM mixed structure, combines historical operation data and fault cases of equipment, calculates each feature weight, and dynamically constructs and updates a health state feature vector library of the equipment, thereby improving the accuracy and timeliness of equipment state evaluation, and improving the reliability of equipment state evaluation. Therefore, managers can find potential faults in advance and take preventive maintenance measures.

The invention relates to the technical field of rail transit monitoring, and discloses a multi-sensor fusion rail transit electric passenger car bogie dynamic performance monitoring method. The method comprises the steps that multi-source heterogeneous sensing data such as bogie vibration acceleration time sequence signals, wheel-rail contact force distribution data, bearing temperature gradient data and structural strain field data are collected; analyzing the vibration signal to extract a frequency band energy feature vector, performing spatial gridding mapping on the contact force data to generate a distribution matrix, and performing time-space alignment fusion on the two to form a primary fusion feature set; positioning a bearing temperature anomaly region through a deep convolutional network, and generating a secondary fusion feature set in combination with a structural strain concentration coefficient; and inputting the two-stage feature set into a dynamic performance evaluation model, outputting a performance degradation index set of scores of axle box bearing wear degree, framework fatigue cumulant, wheel set tread damage and the like, generating a maintenance priority sequence and triggering a preventive maintenance instruction according to the performance degradation index set, and realizing accurate monitoring and efficient operation and maintenance management and control of the dynamic performance of the bogie.

The invention relates to the technical field of numerical control machining, in particular to a numerical control tool wear online monitoring method and system based on multi-sensor fusion, and solves the problems that the monitoring precision is reduced and the false alarm rate is high due to working condition parameter drift. The technical bottlenecks that the discrimination adaptability of a fixed threshold value is insufficient and an offline training model cannot be continuously optimized are overcome. According to the method, the tool wear identification accuracy is effectively improved, the false alarm occurrence frequency is reduced, the residual life prediction reliability is improved, the intelligent monitoring level and the production stability in the numerical control machining process are improved, a reliable technical means is provided for precise management and preventive maintenance of the tool in the whole life cycle, and the method is suitable for popularization and application. Through a dual synchronization mechanism of unifying timestamps of a hardware clock and triggering angles of a main shaft encoder, accurate alignment of multi-sensor data with different sampling frequencies in a time axis and an angle domain is ensured.

The invention relates to the technical field of wind power generation, and discloses a fan blade deformation degree detection method and system, and the method comprises the steps: obtaining laser radar point cloud data, an adaptive image and a high-frequency vibration signal of a fan blade; generating a real-time three-dimensional model of the fan blade; calculating the displacement of the preset key feature points, and generating a global deformation thermodynamic diagram; constructing a dynamic attractor sequence based on the high-frequency vibration signal, and extracting multi-scale fingerprint features through the dynamic attractor sequence; and according to the global deformation thermodynamic diagram, the deformation degree of the fan blade is determined in combination with curvature change, and based on a comparison result of the multi-scale fingerprint features and a preset fingerprint threshold value, a micro-deformation area is positioned and damage early warning is realized. According to the method, the problems of poor data quality, low fusion degree, inaccurate deformation quantification, early warning lag and the like in the prior art are effectively solved, the detection precision, efficiency and reliability are remarkably improved, and comprehensive technical support is provided for preventive maintenance of wind power equipment.

The invention provides a graph self-encoding digital twin construction method for aero-engine vibration overrun fault diagnosis, and belongs to the field of aero-engine digital engineering maintenance and guarantee. The construction method is divided into three parts, namely an engine sensor network state correction diagram, a self-encoding model and a feedback decision, and comprises the following steps: acquiring an engine sensor signal, and selecting proper sample characteristics of data; performing denoising, normalization and resampling preprocessing on each piece of state data, constructing a time window, and dividing a training test set; constructing a graph self-coding digital twinborn body; and performing fault prediction by using the constructed digital twinborn body. According to the method, the vibration overrun fault of the engine can be efficiently identified, the normal working condition and the abnormal state can be accurately distinguished, and the accuracy and reliability of fault identification are remarkably improved, so that timely support is provided for preventive maintenance and operation decision of the engine, and a solid technical guarantee is provided for health management of the aero-engine.

The invention discloses a fault analysis system and method for ship equipment, and the system comprises a multi-source sensing synchronization module which is configured to carry out the phase synchronization of the operation data of multiple equipment through a hardware phase locking mechanism; the multi-physical field modeling module is coupled to the multi-source sensing synchronization module, and is configured to construct a device virtual state field based on the multi-device operation data after phase synchronization, and generate a dynamic parameter representing a multi-device state time sequence imbalance degree; the adaptive diagnostic analysis module comprises a deep learning fault analysis model, and recessive feature extraction parameters and fault evolution time sequence modeling parameters of the deep learning fault analysis model are adaptively adjusted in real time according to dynamic parameters so as to output system-level fault characterization parameters; and the predictive decision-making module is configured to generate decision-making information containing fault development trend prediction and a preventive maintenance window based on the system-level fault characterization parameters. And high-precision correlation diagnosis and predictive maintenance decision-making of early faults of multiple devices of the ship can be realized.

The invention relates to the technical field of photovoltaic power operation and maintenance, in particular to a photovoltaic panel cleaning robot fault detection method and system, and the method comprises the steps: building a health state portrait based on the multi-mode time sequence data of robot cleaning operation, and comparing an ideal operation behavior model to generate an efficiency wear coefficient; in combination with the photovoltaic panel pollution characteristic parameters and the environment disturbance parameters of the current cleaning task, analyzing the mapping relation between the efficiency wear coefficient and the corresponding task scene, and obtaining a scene-driven part vulnerability map; and based on the component vulnerability map and a preset operation and maintenance knowledge base, deducing expected load impact on each subsystem of the robot caused by execution of a subsequent planning task, and generating a decision instruction set according to the expected load impact, including maintenance priority and operation suggestions. According to the invention, the fault risk of the photovoltaic panel cleaning robot executing a future cleaning task can be assessed prospectively, preventive maintenance is planned in advance, task interruption and non-planned shutdown are avoided, and the operation and maintenance efficiency of a photovoltaic power station is improved.

The invention discloses an intelligent operation and maintenance system and method based on flow battery digital twinning, and relates to the technical field of flow battery energy storage. According to the method, through multi-source heterogeneous data fusion, electrical, fluid and environmental parameters and image data are collected and uniformly processed; constructing a mixed index database to add spatio-temporal information; optimizing the neural network through a PSO-BP algorithm to generate a space fusion model; constructing a multi-physics field real-time simulation model by combining data driving and a mechanism model; and finally, dynamic interaction and fault prediction of the physical and virtual systems are realized through three-dimensional virtual mapping. The system comprises a data preprocessing module, a mixed index database and the like. Unified fusion and efficient management of multi-source heterogeneous data are achieved, and the problem of data fragmentation is solved; a multi-physical-field state is simulated in real time through a digital twinning technology, and a fault is accurately predicted; and preventive maintenance is supported, the operation and maintenance cost is reduced, and the safety and economic benefits of the flow battery system are improved.

The invention relates to the technical field of offshore wind turbine generators, and discloses an offshore wind turbine generator running state evaluation method. Comprising the steps of deploying a sensor network on an offshore wind turbine generator, carrying out data acquisition and preprocessing, detecting states of key components, carrying out overall state evaluation, early warning and decision support, and carrying out preventive overhaul and maintenance, summarization and recording. According to the operation state evaluation method for the offshore wind turbine generator set, comprehensive monitoring and scientific management of the offshore wind turbine generator set and auxiliary equipment are realized by integrating technologies of multi-source data acquisition, state monitoring and early warning and the like, and the limitation that information islands depend on artificial experience in a traditional operation and maintenance mode is broken through depending on an intelligent sensing network and big data analysis, so that the operation state evaluation method for the offshore wind turbine generator set is realized. The digital operation and maintenance management capability of the whole life cycle of the wind power plant equipment is established, and the fault early warning accuracy and the operation and maintenance efficiency are remarkably improved through intelligent monitoring.

The invention relates to a self-adaptive optimization decision-making method for preventive maintenance opportunity of a road surface, which comprises the following steps of: establishing a road surface performance prediction model based on multi-source data fusion based on historical road surface performance data, traffic load, climate environment and material structure characteristics; based on the performance data of the pavement before and after maintenance construction, extracting the instantaneous performance resilience value after maintenance completion and the performance attenuation rate after maintenance, and establishing a maintenance effect prediction model; constructing a double-layer optimization decision model taking the total cost minimization of the whole life cycle as an optimization target; the upper layer takes the maintenance opportunity threshold value as a decision variable to generate a corresponding maintenance demand; the lower layer solves the optimal maintenance schedule under the corresponding threshold value; in the lower-layer solving process, a maintenance effect prediction model is called to predict the maintenance effect; and an optimal maintenance opportunity threshold value is obtained through simulation optimization search. The method aims to realize closed-loop coupling of performance prediction, maintenance effect quantification and maintenance decision, and minimize the total cost of the whole life cycle on the premise of ensuring the pavement service level.

The invention belongs to the technical field of wind power, and provides a computer-aided new energy wind turbine generator maintenance method and system, and the method comprises the steps: obtaining the operation condition data of a new energy wind turbine generator, and constructing a wind turbine generator database storing multi-source knowledge data; according to the operation condition data and a wind turbine generator database, performing fault diagnosis on the new energy wind turbine generator by adopting model prediction in combination with rules and case reasoning to obtain a fault diagnosis result; inputting the fault diagnosis result and the operation condition data into a pre-constructed health assessment model to obtain a health assessment result; and according to the health assessment result and a preset maintenance priority, adopting an optimization algorithm to dynamically generate preventive maintenance plan information. According to the scheme provided by the invention, a more effective and targeted data basis can be provided for maintenance personnel for maintenance, and the timeliness, reliability and high efficiency of a maintenance link are improved.

The invention discloses a current and vibration collaborative perception diagnosis system and method for a rotor turn-to-turn short circuit fault, relates to the field of equipment diagnosis, and realizes full-life-cycle accurate management of the generator rotor turn-to-turn short circuit fault by constructing an intelligent diagnosis and maintenance system driven by multi-dimensional data. The fault mapping model constructed based on historical fault data can quickly identify the fault type and evaluate the severity through current and vibration signal features collected in real time, and the accuracy and timeliness of fault diagnosis are significantly improved; secondly, interval iterative optimization is implemented through preventive measures, and in combination with dynamic prediction of short-circuit occurrence times and repair cost, personalized customization of preventive maintenance strategies is realized, and the full-life-cycle maintenance cost is effectively reduced while the reliability of equipment is guaranteed; in addition, due to introduction of a hierarchical maintenance strategy, the problem of excessive maintenance or insufficient maintenance is avoided, and the use efficiency of maintenance resources is improved.

The invention discloses a fan fault analysis and operation and maintenance decision support method based on a new energy monitoring system, belongs to the field of new energy power generation monitoring, and aims at solving the problems that an existing system is low in fault recognition precision, depends on artificial experience and is not fully mined in data value. According to the method, a wind turbine generator SCADA system, meteorological data and operation log multi-source data are integrated, and a five-dimensional fault feature tag set of electrical and mechanical faults is constructed; after module pre-classification, a DBSCAN clustering algorithm is adopted for analysis, and samples with the similarity matching degree smaller than 60% of a new clustering center and an original clustering center serve as newly-added typical cases to update a fault processing knowledge manual; when the matching between the unit parameters and typical faults is greater than or equal to 80%, automatic early warning is carried out and a processing scheme is pushed; carrying out preventive maintenance in combination with a time sequence model, optimizing a spare part inventory according to a weighted model, and pushing a case when the matching between a fault and a historical case is greater than 90%; the method improves the fault diagnosis precision, shortens the processing time, reduces the operation and maintenance cost, and is suitable for various manufacturer models.

The invention discloses a fault diagnosis and self-healing control method, system, equipment and medium for a flexible interconnection device of a power distribution network, and belongs to the technical field of fault diagnosis and control of the power distribution network, and the method comprises the steps: building a system state vector, collecting operation parameters of the power distribution network, and carrying out the weighted fusion; performing quality inspection on the original data based on the acquired operation parameters, and dynamically adjusting model parameters through digital twin synchronization and model parameter updating; deep learning is carried out to carry out multi-level fault feature extraction, and multi-scale feature fusion is carried out; a fault mode identification module is started, and fault probability is calculated for fault classification; evaluating the severity of the fault according to a fault diagnosis result, selecting an optimal control strategy, and carrying out self-healing control; and fault early warning is started to estimate a future fault occurrence probability, and early warning information of different levels is issued to perform preventive maintenance. According to the invention, full-process automation and intelligentization from data perception and intelligent diagnosis to active control and prospective maintenance are realized.

The invention relates to the technical field of urban municipal engineering safety, and provides an urban gas pipe network space-time risk assessment method and system, and the method comprises the steps: constructing a pipe network digital twinborn model fusing multi-source data; aiming at the metal pipeline, calculating the effective wall thickness of the pipeline by applying the nonlinear time-varying corrosion model corrected by the soil corrosivity index; calculating the biaxial stress of the pipe wall through a mechanical model considering the coupling effect of internal pressure, soil, traffic, temperature and frost load based on the effective wall thickness; according to the material of the pipeline, calculating a dynamic safety coefficient by adopting a criterion based on fracture mechanics or allowable stress; and finally, risk levels are divided according to the safety coefficient, and space-time visual display is carried out on a GIS platform. According to the method, the defects that a traditional method depends on historical data, models are simplified and space-time dynamic analysis is lacked are overcome, the pipe network risk can be accurately quantified and predicted, and a scientific basis is provided for preventive maintenance.

The invention provides an equipment predictive maintenance method and system based on residual life quantiles, and belongs to the field of equipment maintenance and reliability engineering. According to the strategy, firstly, a Gamma process is adopted to construct an equipment degradation model, and Beta distribution is adopted to establish a residual degradation amount model; then designing a discrete check strategy based on the residual life quantile, determining a calculation mode of a discrete check interval, and formulating a maintenance decision rule; calculating maintenance related cost, including preventive maintenance cost and operation cost; and finally, constructing an optimization model with the goal of minimizing the average cost rate, and determining optimal maintenance parameters by adopting a discrete approximate iteration method. The method can accurately grasp the state change of the equipment, formulate a scientific and reasonable maintenance strategy, effectively reduce the maintenance cost, improve the reliability of the equipment, and is suitable for predictive maintenance of various industrial equipment.

The invention discloses a concrete bridge preventive maintenance decision-making method based on degradation prediction, and relates to the technical field of bridge degradation prediction maintenance, and the method comprises the steps: collecting the degradation characteristic data of a concrete bridge through a bridge detection device; according to the method, various types of degradation characteristic data are collected through the bridge detection equipment, the standardized bridge state characteristic vector set is generated, the data-mechanism fusion inversion model is adopted, the actually collected data and the inherent mechanism of bridge degradation are combined, the estimation result is made to better conform to the actual situation, and the estimation accuracy is improved. Dynamic data assimilation is carried out according to a state space model, a probabilistic estimation set of a degradation driving state is input into a predetermined bridge degradation mechanism model for long-term deterministic simulation prediction to generate a plurality of future performance degradation paths, and all the paths form a degradation track cloud picture reflecting prediction uncertainty; the optimal maintenance strategy set can reduce the expected life cycle cost to the maximum extent on the premise that the bridge maintenance requirement is met.