76 results about "Dynamic maintenance" patented technology

The invention discloses a mold life prediction and maintenance strategy making method based on big data analysis, and particularly relates to the field of mold life prediction and maintenance, and the method comprises the following steps: collecting and associating multi-source heterogeneous data in a mold production process; comprising process parameter time sequence data, on-line monitoring time sequence data and structured and image data of off-line inspection; performing time domain alignment and collaborative feature extraction on the data, and constructing a structured training data set taking a production cycle as a unit; inputting the data set into a dynamic evolution prediction model, outputting a mold health state score, and generating a dynamic maintenance strategy through a multi-objective optimization model in combination with production context information; and finally, visually displaying the strategy, converting the strategy into a control instruction, issuing the control instruction to a production management system, and automatically triggering maintenance execution. The method can realize high-precision life prediction, dynamically optimize the maintenance decision, significantly improve the service life and production efficiency of the mold, and reduce the maintenance cost and production risk.

The invention discloses an intelligent concrete automatic curing system and method based on real-time monitoring, and the system comprises a multi-parameter sensing module which collects the environment parameters and internal structure parameters of a concrete structure through a plurality of sensors and label identification codes, and an edge calculation module which is used for carrying out the preprocessing, feature extraction and emergency decision-making of collected data. The cloud intelligent module is used for storing data, establishing a prediction model and generating a dynamic maintenance strategy; the maintenance execution module is used for adjusting and executing spraying, temperature control or crack repair operation according to the maintenance strategy; by integrating structural strain, crack detection and environment air pressure monitoring, the limitation of traditional single temperature and humidity monitoring is broken through, multi-dimensional sensing and accurate diagnosis of concrete are achieved, meanwhile, the system combines a physical model and a data driving model, the self-adaptive optimization capability is achieved, and the system is suitable for large-scale popularization and application. And the edge gateway is adopted to process emergencies, and the cloud platform is responsible for global optimization, so that the system gives consideration to the real-time performance and the calculation depth, and the maintenance efficiency and the concrete quality are effectively improved.

The invention relates to a database table field description intelligent generation and dynamic maintenance method and device based on RAG and a large model. The method comprises the following steps: constructing a domain-based dynamic knowledge base system through multi-source corpus collection, domain adaptation vectorization model processing and a dynamic knowledge base construction and updating process; receiving a description generation request of a table or a field, recalling related knowledge from the knowledge base through an RAG technology, assembling cue words, and then calling a large model for reasoning to generate a description result; the generated description result is confirmed through manual auditing, the knowledge base is updated according to confirmation feedback, and continuous optimization of the knowledge base is achieved. According to the method provided by the invention, the problems of low accuracy, high maintenance cost, poor interpretability and the like in a traditional method are effectively solved by constructing a field-enhanced RAG architecture and combining a three-layer knowledge system and a self-adaptive prompt framework which can be dynamically adjusted according to a business scene, and a new technical implementation path is provided for table and field description in intelligent data governance.

The invention discloses a dynamic correction method and system for equipment fault mode occurrence degree, and belongs to the technical field of power plant electrical overhaul and maintenance, and the method comprises the steps: collecting operation parameters, maintenance records and historical fault data of target electrical equipment, and recognizing a potential fault mode set of the target electrical equipment; failure mode and influence analysis is carried out on each failure mode, and the initial occurrence degree, severity and detectability of each failure mode are determined; calculating an initial risk priority number of each fault mode, and determining an initial risk according to a preset risk grade division rule; dynamically adjusting the initial occurrence degree, recalculating the updated risk priority number, and generating a corrected risk level; and constructing a dynamic maintenance decision model, and outputting a maintenance priority and a maintenance time window for each fault mode. According to the method, the defects of real-time performance, comprehensiveness and flexibility of electrical operation and maintenance of the power plant are overcome, and efficient and accurate data support is provided for operation and maintenance of electrical equipment.

The invention discloses a geological archive structure integrity guarantee method and system based on map dynamic maintenance, and the method comprises the steps: obtaining an archive circulation path which is directly and indirectly connected with an archive for peripheral associated data marked as an undeletable archive, analyzing a potential breaking point in the path, and obtaining a risk region which may cause a data isolation problem; performing deep scanning on archive data in the risk area to obtain content features and historical circulation records of archives, judging whether the content features and the historical circulation records meet data accuracy standards or not, and determining a non-key archive list needing to be deleted; according to the non-key file list, analyzing the operation influence range of each file to be deleted, obtaining potential impact on the stability of the whole system after the file is deleted, and if it is judged that the information network integrity is damaged due to deletion, moving the file out of the deletion list; and for the finally determined deletion list, acquiring backup data and a flow path log of the archive, and updating the archive connection atlas through a dynamic maintenance mechanism to obtain a new information network structure.

The invention discloses a wire feeding processing intelligent operation and maintenance system based on cloud computing, and relates to the technical field of industrial automation and cloud computing. The problems that in an existing system, due to data transmission delay, abnormal response of equipment is not timely, system stability is affected by unreasonable computing resource scheduling, and the operation and maintenance effect is poor due to poor adaptability of a dynamic maintenance strategy are solved. Comprising an edge layer, a transmission layer, a scheduling layer, a cloud layer and an application layer. The edge layer collects data and makes an emergency response; the transmission layer optimizes data interaction based on network parameters; the scheduling layer balances task allocation through a deep reinforcement learning algorithm; the cloud layer constructs a prediction model and generates a dynamic maintenance strategy according to a result; the application layer visually displays and distributes operation and maintenance tasks; according to the method, the fault response speed, the resource scheduling balance and the maintenance strategy adaptivity are greatly improved, and the production continuity and the product quality are remarkably improved.

The invention discloses an equipment full-life-cycle intelligent operation and maintenance management method and system based on AI deep learning. The method comprises the following steps: collecting multi-source heterogeneous data generated in each stage in a full life cycle of equipment, deeply exploring a knowledge network for the heterogeneous data through semantics, converging and fusing associated equipment entities, constructing a triple knowledge network, and obtaining an equipment knowledge graph network; constructing an equipment digital twinborn body corresponding to the equipment entity in a high-fidelity manner through the digital twinborn virtual mapping and the equipment experiment sand table; carrying out equipment operation simulation deduction according to the real-time monitoring data to obtain digital twin simulation deduction data; machine learning deep intelligent analysis is carried out according to the digital twinborn simulation deduction data, the equipment health state is evaluated and predicted, and equipment degradation track life prediction information is obtained; and querying equipment knowledge graph information according to an equipment knowledge graph network, calibrating digital twin simulation deduction data, and reasoning and generating an equipment dynamic maintenance decision by referring to equipment degradation track life prediction information.

The invention is suitable for the technical field of industrial dust remover equipment operation monitoring and maintenance, and provides an industrial dust remover operation state monitoring method and system, and the method comprises the steps: obtaining an initial maintenance period of a target dust remover, extracting a plurality of historical samples which have the same type as the target dust remover, but have different high-chlorine treatment frequencies and the same preset work cycle from the database of the target dust removal plant; by constructing the comprehensive corrosion index and analyzing the change trend of the comprehensive corrosion index along with the increase of the high-chlorine treatment frequency, the key inflection point of rapid development of corrosion caused by damage of the protective film can be captured, the future high-chlorine treatment frequency is predicted in combination with the database, and the relative deviation value of the comprehensive corrosion index and the critical point is calculated and predicted. A maintenance period correction factor is generated, the initial maintenance period is revised in real time, and prospective early warning and dynamic maintenance of the potential accelerated corrosion risk of the equipment are achieved.

The invention discloses an RCM-based power plant maintenance strategy making method and a related device, and belongs to the technical field of power plant maintenance. The method comprises the following steps: constructing a maintenance database of power plant equipment according to historical maintenance records; acquiring operation parameters of the power plant equipment, inputting the operation parameters into a pre-established state analysis model, and outputting a fault type analysis result; matching the fault type analysis result with a maintenance database to generate a candidate maintenance strategy set; and based on the candidate maintenance strategy set, calculating a risk priority number RPN corresponding to each strategy, and generating a dynamic maintenance plan based on the RCM according to the risk priority number RPN. The maintenance plan can be dynamically generated according to the real-time operation state of the equipment and the fault type analysis result, and the flexibility and adaptability of the maintenance plan are improved.

The invention relates to the technical field of industrial equipment control, and discloses a dynamic maintenance management method and system based on the full life cycle of test equipment, and the method comprises the steps: obtaining a time domain vibration signal and torque current data during the operation of the equipment through a high-frequency vibration sensor and a current loop feedback interface; calculating an accumulated damage gradient parameter representing the physical recession degree of the mechanical transmission chain; the parameters are input into a preset damage stress coupling model to solve the maximum radial impact force allowed to be borne by the damaged part, and the impact force is mapped into a dynamic capability boundary for carrying out logic constraint on the motion planning instruction; and the motion controller reconstructs an ideal speed curve by using the boundary in an interpolation period, and reshapes a torque load spectrum output to a servo motor by reducing the jump of acceleration and deceleration sections. The problem that an existing static control strategy is mismatched with the dynamic aging characteristic of equipment is solved.

The invention belongs to the technical field of boiler maintenance, and particularly relates to a boiler four-pipe foreign matter blockage early warning method and system. The method comprises the steps of obtaining boiler four-tube foreign matter blockage historical data; collecting boiler four-tube real-time state data of a reheater, an induced draft fan and a flame detection cooling fan; a fault mode-influence-risk mapping relation is established by adopting FMEA based on historical data, and a risk assessment framework is formed; generating an initial risk level; training a machine learning algorithm model by adopting historical data and generating a blockage probability; and dynamically adjusting the initial risk level according to the blockage probability, generating a final risk level and outputting a corresponding maintenance strategy. The system comprises a data acquisition module, an equipment monitoring module, a risk division module, a risk assessment module, a jam prediction module and a decision module. According to the scheme, the FMEA and the machine learning algorithm are fused, early warning, accurate risk assessment and dynamic maintenance decision making of boiler foreign matter blockage are achieved, and boiler operation safety and maintenance efficiency are improved.

The invention relates to the technical field of predictive maintenance and health management of industrial equipment, in particular to an intelligent electromechanical integrated monitoring system based on the industrial internet, which comprises a data acquisition module, a data processing module, a data processing module and a data processing module, and is characterized in that the data acquisition module acquires an equipment dynamic operation parameter set, inherent physical attribute parameters and historical maintenance data of target electromechanical equipment; calling preset process topology data, economic loss data and maintenance cost data; the state evaluation module iteratively calculates an accumulated degradation index representing the health condition of the equipment; the importance quantification module calculates a process importance coefficient representing the criticality of the equipment in the production line based on the process topology data; the risk calculation module generates a production risk degree which quantifies the shutdown influence; the decision generation module generates a dynamic maintenance decision instruction; according to the invention, systematic risk cognition from the individual equipment to the overall production line is realized, extensive management of all equipment is avoided, and allocation of maintenance resources is more strategic and targeted.

The invention discloses a dynamic maintenance method for a distribution network transient recording event sample library and computer equipment. The method comprises the steps of obtaining a first preset database; receiving a maintenance operation executed on the first preset database based on the target account; based on the maintenance operation, a log is generated and stored in a second preset database, and the log comprises operation time corresponding to the maintenance operation, an identifier corresponding to the power distribution network transient recording event sample and a target account identifier; based on the preset interval duration, whether an abnormal identifier exists in a first preset database is detected, and the abnormal identifier represents that the identifier of the corresponding power distribution network transient recording event sample does not exist; and under the condition that the abnormal identifier does not exist in the first preset database, determining that the maintenance operation is executed successfully. According to the method, the technical problem that certain risks exist in data errors due to implicit association of waveform data and label information when a power distribution network transient recording event sample library is maintained at present is solved.

The invention relates to the field of elevator maintenance, and discloses an elevator dynamic maintenance period optimization method and system combined with Internet of Things analysis, and the method comprises the following steps: collecting multi-modal data of an elevator in a manner of deploying a heterogeneous sensor array, and determining elevator part damage root cause associated nodes; and the aim of constructing an elevator fault prediction model is fulfilled by combining a federal learning mode. And through the elevator fault prediction model, risk simulation analysis is carried out in combination with a parallel Monte Carlo simulation method, and a periodic optimization optimal solution of elevator dynamic maintenance is output according to an analysis result. According to the method, the continuity and intelligence of data inspection can be realized in combination with an advanced data analysis algorithm, the operation and maintenance cost is remarkably reduced, and the resource utilization efficiency is improved.

The invention belongs to the field of hydroelectric generating set RCM maintenance, and discloses a hydroelectric generating set RCM maintenance strategy determination method and system based on risk priority, and the method comprises the steps: building an FMEA analysis database based on the historical operation data, fault event ledger and equipment state monitoring data of a hydroelectric generating set; performing quantitative evaluation on each fault mode of the FMEA analysis database from occurrence degree O, severity S and undetectability D, and calculating a risk priority number RPN of each fault mode; risk grades are divided according to the risk priority number RPN, a dynamic maintenance strategy is generated based on the risk grades, the dynamic maintenance strategy is output to the operation and maintenance management system, the actual operation condition and fault risks of equipment are fully considered, the reliability and safety of the hydroelectric generating set are effectively improved, the maintenance cost is reduced, and the non-planned downtime is shortened.

The invention provides a method and a system for dynamically maintaining a Token, computing equipment and a computer readable storage medium, in the method, after a server generates a first Token after login verification of a client is passed and before the first Token is returned to the client, the first Token is stored in a first database, and an index mapping table of a unique identifier of the Token is established; 'pseudo-stateful 'management of the Token is realized by intercepting a client request; when the risk of Token leakage occurs, the corresponding record in the first database can be directly deleted or the interface is called to forcibly expire the Token. Furthermore, all active Tokens of the user are stored by taking a user name as a key to form a global view of the login user, an automatic cleaning function, manual revocation and a self-defined capacity control strategy are integrated through a dynamic maintenance strategy, the Token number upper limit and validity period rule of a single user are supported to be dynamically adjusted, and flexible management of concurrent login of multiple devices is realized.

The invention discloses a die full life cycle management system based on the Internet of Things, and relates to the technical field of die management, and the die full life cycle management system comprises the steps: obtaining a pre-correlation parameter and a post-correlation parameter of a process in each stage in a die full life cycle, and obtaining a pre-correlation feature and a post-correlation feature; dynamically maintaining equipment in the process based on the real-time parameters; the method is used for solving the problems that in an existing mold full-life-cycle management method, the state of a mold is predicted in a one-sided mode only through collection of single data, and when the process where the mold is located changes and other variables influence the state of the mold, the relation between the single data and the variables cannot be effectively judged, and the mold state cannot be effectively predicted. Therefore, the problem that effective management cannot be performed based on the subsequent state of the mold is solved.

This invention provides a dynamic management method and system for multi-level assessment indicators of the resilience of nearshore bridges throughout their entire life cycle, relating to the field of structural resilience assessment and management technology. The method includes: acquiring multi-source heterogeneous maintenance data streams throughout the entire life cycle of the nearshore bridge; processing these data streams through time-series alignment and feature dimensionality reduction mapping to obtain a basic feature matrix of the bridge's service status; and performing conditional routing and data stream processing based on this basic feature matrix to obtain corrosion evolution calculation potential field distribution data, structural dynamic stiffness attenuation tensor, and structural life-cycle degradation confidence distribution vector. This invention accurately obtains resilience indices that truly reflect the bridge's resistance to degradation through multi-step quantification calculations, clarifies the sensitivity differences of assessment nodes, formulates differentiated dynamic maintenance strategies, and constructs a closed-loop management system throughout the entire process, thereby achieving intelligent bridge maintenance, improving maintenance efficiency, and reducing costs.

The application provides an image processing-based intelligent analysis method and platform for a power system, a control collection device acquires icing data of a power line in a monitoring destination, determines a heavy icing point according to the icing data, determines a dynamic maintenance range of a target line according to a working range of a cooperative maintenance device, acquires a dynamic working duration of the cooperative maintenance device based on the heavy icing point in the dynamic maintenance range, acquires deicing detection data of the cooperative maintenance device based on the dynamic maintenance range and the dynamic working duration, updates the dynamic maintenance range and the dynamic working duration according to the deicing detection data, and obtains linkage deicing data of the cooperative maintenance device.

The embodiment of the invention discloses an equipment maintenance method and device, electronic equipment and a computer readable storage medium, and relates to the technical field of computers. The method comprises the following steps: acquiring historical event data of equipment; inputting the historical event data into a maintenance time prediction model to obtain predicted dynamic maintenance time; generating a dynamic maintenance plan according to the dynamic maintenance time; and maintaining the equipment according to the dynamic maintenance plan. Therefore, the scheme can improve the probability of equipment maintenance.

The invention relates to a bolted connection local contact stiffness sudden change threshold prediction method, which comprises the following steps of: 1, constructing a three-dimensional model of a bolted connection composite board, and performing finite element simulation analysis on the three-dimensional model; 2, performing preliminary screening and normalization on data obtained by modal analysis and harmonic response analysis; 3, constructing a machine learning prediction model by using the training set; 4, the precision of the machine learning prediction model is tested and verified through the test set, and evaluation is carried out through the evaluation indexes. According to the method, the time cost and complexity of manual testing are reduced, the dynamic overhaul period can be formulated according to the predicted threshold value cycle index, and the monitoring efficiency and the production efficiency are improved.

This invention presents an intelligent global dynamic maintenance method for multi-component systems considering imperfect maintenance, based on deep reinforcement learning. First, it acquires the degradation observation sequence of each component through periodic inspections, establishes a stochastic process model of component degradation, and achieves iterative RUL prediction through offline training and online Bayesian updates. Then, it determines the baseline predictive maintenance timing and, considering imperfect maintenance improvement factors, determines the baseline delayed maintenance time. Simultaneously, it constructs a maintenance timing deviation penalty cost model for group maintenance. Next, it uses the PPO algorithm to perform rolling optimization on the dynamic global group maintenance (DGGM) to output maintenance combinations and maintenance execution times. When a sudden failure occurs during operation, dynamic opportunistic imperfect maintenance (DOIM) is triggered, performing corrective maintenance on the failed component and determining the opportunistic maintenance component set, synchronously updating subsequent maintenance plans, thus achieving intelligent global dynamic maintenance decision-making in an infinite time domain. This invention realizes joint optimization of predictive and opportunistic maintenance for multi-component systems.

The utility model discloses a kind of along empty roadway extraction borehole full cycle strict hole sealing structure, belong to coal mine gas extraction engineering technical field.Aiming at the problems such as insufficient shallow grouting pressure, hole sealing section is vulnerable to stress shear damage, pipe bottom hole collapse plugging and lack of failure remedial measures in existing two-plugging one-injection hole sealing structure in along empty roadway, full cycle sealing scheme is proposed.The structure divides extraction borehole into pressure-relief section and primary hole sealing section, pressure-relief section is left empty as deformation buffer, primary hole sealing section is deeply placed at more than 11 meters from roadway coal wall to avoid stress peak area, and forms grouting area by two hole sealing capsules;It is also provided with secondary grouting precision plugging area and later three-plugging two-injection reinforcing structure.The structure can actively avoid harmful stress area, realize mid-term dynamic maintenance and later whole-hole strengthening, significantly improve hole sealing reliability and extraction concentration, prolong borehole life and reduce hole repair cost.

The application discloses an elevator on-demand maintenance method based on dynamic prediction, which comprehensively considers the running state data and use condition data of the elevator, utilizes an elevator health degree evaluation model based on an LSTM algorithm, realizes evaluation on the health degree of the elevator, and obtains a maintenance priority evaluation result accordingly, and further generates a maintenance strategy; considering the maintenance resource limitation, the maintenance strategy is optimized according to the availability of maintenance personnel, tools and equipment and the maintenance cost; in the process of actual execution of the maintenance strategy, the running state of the elevator, the maintenance plan and the execution condition are monitored; and according to the feedback information, the maintenance strategy is optimized by using an optimization algorithm. The method can improve the maintenance efficiency and safety of the elevator, reduce resource waste, and provide personalized and dynamic maintenance strategies.

The invention discloses a wind power intelligent operation and maintenance management method and system based on deep learning. The method comprises the following steps: acquiring a graph structure data set; based on the graph structure data set, modeling a space-time dependency relationship in the fan group and between the fan and the environment by using a space-time graph neural network combined with a multi-head attention mechanism and a GCN graph convolutional network to obtain space-time features; performing feature alignment and fusion through a cross-modal attention mechanism to obtain fusion features; inputting the fusion features into a GRU gating circulation network, outputting residual effective life probability distribution of the wind turbine generator components and dynamic risk levels of multiple types of faults in future 72 hours, and obtaining a prediction result; and based on a deep reinforcement learning model, taking the prediction result as input, taking multi-objective optimization as rewards, and generating a dynamic maintenance decision scheme. The operation and maintenance cost is reduced, the unit availability rate is improved, and a large-scale wind power cluster operation and maintenance scene is adapted.

The embodiment of the invention provides a power grid equipment intelligent maintenance information generation method and system, and the method can integrate a multi-source data set into equipment state information after the multi-source data set is obtained. And performing fault prediction on the power grid equipment according to the equipment state information based on a machine learning algorithm so as to generate an overhaul identification result, thereby generating intelligent overhaul information including an equipment maintenance plan according to the overhaul identification result. According to the method, equipment operation data, environment data and historical fault data are integrated, accurate prediction of equipment faults and dynamic maintenance strategy optimization are achieved through a machine learning algorithm, and therefore the maintenance efficiency and reliability of power grid equipment are improved, and the problem that the maintenance efficiency of the power grid equipment is low is solved. According to the method, the system operation state under the equipment maintenance plan can be simulated through the virtual model, the intelligent maintenance information is dynamically adjusted, the maintenance cost is reduced, and the power failure time is shortened.

The invention discloses a staged drainage system of a large ore storage yard and a construction and dynamic maintenance method of the staged drainage system, and belongs to the technical field of large bulk cargo storage yard foundation treatment and dynamic drainage engineering. The system comprises a peripheral closure system, an internal seepage and drainage transition system, an under-road communication system and a permanent drainage system. The core of the construction and dynamic maintenance method is as follows: a flexible gravel ditch is adopted to adapt to differential settlement in the initial stage; monitoring in the settlement period, and dynamically backfilling and maintaining the settlement area by adopting a graded material; and after stabilization, the gravel ditch is reconstructed into a reinforced concrete ditch in situ. According to the method, foundation settlement is effectively dealt with through the strategy of'firstly softening and then rigidness, dynamic monitoring and timely upgrading ', drainage failure caused by foundation settlement in a traditional method is avoided, efficient and reliable operation of a drainage system in the whole life cycle is achieved, and safety and economical efficiency are remarkably improved. The method is suitable for drainage of large material piles such as ore and coal under the rainstorm working condition, and has remarkable safety and economic benefits.

The invention relates to the technical field of roadway support engineering, and discloses an intelligent adaptive roadway support and dynamic maintenance integrated process. According to the technology, through geological environment and stress global detection, a multi-dimensional roadway digital model is constructed, support parameters are optimized based on historical data and real-time monitoring data, a regional stress discretization sorting support strategy is adopted, and dynamic monitoring feedback and intelligent maintenance decision are combined. And accurate adaptation of a supporting scheme and full-life-cycle dynamic management of a supporting structure are realized. The technical problems that in the prior art, support parameter determination is insufficient in scientificity, maintenance lacks pertinence, and roadway stress changes cannot be dynamically adapted are solved, the stability and safety of roadway support are remarkably improved, the maintenance cost and the accident risk are reduced, and the method is suitable for roadway engineering under various complex geological conditions.

The present application relates to the technical field of equipment maintenance, and more particularly to an intelligent predictive equipment maintenance system, comprising a data acquisition module, a data processing module, a maintenance decision module, a maintenance execution module, a user interaction module and a closed-loop optimization module. The data acquisition module collects equipment operating parameters through the deployment of a sensor array to generate standardized data streams; the data processing module extracts time sequence features and performs state deduction through a virtual model to output fault analysis results; the maintenance decision module calls enterprise resource information for multi-objective optimization to generate a dynamic maintenance plan; the maintenance execution module assists in guiding the execution of maintenance through augmented reality and records data; the user interaction module dynamically displays equipment status and risk information; and the closed-loop optimization module updates model parameters based on feedback data. The system realizes predictive maintenance under data driving through module collaboration, effectively reduces unplanned downtime and resource waste, and improves fault diagnosis accuracy and operation and maintenance efficiency.

Techniques for autonomous maintenance management (AMM) of an industrial facility are described. In one aspect, an AMM system monitors operational data values and an event associated with an equipment to determine key performance indicators and trend patterns associated with the equipment, where the event is characterized by deviations in the operational data values from a pre-determined threshold value, and where the trend pattern is indicative at least of operational history of the equipment. The KPIs and trend patterns are analysed to predict a behaviour of the equipment, based on which a dynamic maintenance schedule is generated, where the dynamic maintenance schedule includes a task to be executed for maintenance of the equipment. Further, the system obtains personnel information of the industrial facility to identify personnel to perform the task and accordingly issues a maintenance work order in correspondence to the dynamic maintenance schedule for maintenance of the equipment.