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Maintenance strategies aim at reduction of the operation and maintenance cost of wind turbines. An opportunistic maintenance strategy offers cost advantages over the traditional preventive replacement maintenance strategy by considering cost dependency among maintenance activities. A new opportunistic maintenance strategy of wind turbines is proposed here. First, the proposed strategy includes two sets of reliability thresholds to handle subassemblies those are in operational and failed wind turbines. Using the reliability thresholds, ten maintenance modes and their service conditions are presented. Second, the Weibull distribution with an age reduction factor is introduced to describe reliability variation of subassemblies under imperfect maintenance. Third, the maintenance cost in each maintenance mode is expressed by a reliability variation‐based function that is dependent on the stage of maintenance activities. Fourth, due to the randomness of failures, the expected total maintenance cost is determined with Monte Carlo simulation. The proposed approach has been validated with the data provided by a wind farm located in Northern China.

There is a close relationship between the operation safety and the application of training equipment. If the relationship is not handled well, it will lead to serious problems, even the Conflicts. So designing training equipment management information system is extremely urgent. First, the main training categories are carded, such as drivers training, construction and maintenance, daily safety management, canteen safety, etc., and the basic flow chart of the 4 types of training are drawn; Second, the training equipment management database Train_Database is constructed based on training process, equipment involved, trainers, contingency plans of the 4 kinds of raw data, which lay the foundation for the follow-up of the management information system design and development; Third, the training equipment declaration and management system is developed, which is called Training_Equipment_MS, and the main modules are: equipment resource information management module, equipment declaration module, equipment audit module, safety check form filling module, equipment declaration results publicity module, etc. Finally, the functions of each module are shown in details. It has good practical guidance to the application and operation of rail transit, which can reduce accidents and hidden danger in the course of training.

With the increase in the number of machine tools in the workshop and the increase in machine tool operating data, the traditional maintenance mode cannot meet the needs of enterprises for efficient and stable production in the workshop. Based on big data components such as Flink, Kafka, and HBase, a high-performance real-time monitoring system for multiple machine tools is constructed. The system collects data by adding sensors and the communication protocol of the CNC system, and writes the collected data into the message queue Kafka. The real-time monitoring module is built with Flink as the core, and consumes data from Kafka as a Kafka consumer. After Flink completes the preprocessing and statistics of the data, it stores the data in HBase, and realizes the visual display of the monitoring data through visual components. Experimental test results show that the system runs stably, has reliable performance, and has good scalability, which can effectively improve workshop monitoring efficiency and reduce enterprise operating costs.

A reasonable maintenance time is vital in the metro shield tunnel maintenance to keep good structural performance and reduce maintenance cost. Lacking predictability, the existing maintenance mode cannot effectively resolve the contradiction between the limited maintenance cost and the growing maintenance need in the future. In this study, the degradation model of metro shield tunnels in soft soils was studied to predict the long-term serviceability. Based on that, a method was proposed to decide the optimal time of preventive maintenance for metro shield tunnels. It was found that the tunnel decay process can be expressed by the inverse exponential function-based prediction model. The cost-benefit analysis method was adopted to determine the optimal preventive maintenance time, considering the maintenance time, the maintenance cost and the maintenance measures adopted. The flow chart of this method and an application example were presented. Results can help metro operation owners to develop more scientific and reasonable preventive maintenance plans for shield tunnels in soft soils.

Current maintenance mode for high-speed railway (HSR) power equipment is so outdated that can hardly adapt to the high-standard modern HSR. Therefore, a new possibility is proposed in this article to update the obsoleting maintenance mode of the HSR power equipment by adopting both predictive maintenance and proactive maintenance. With the combination of data-driven (predictive) and model-based (proactive) approaches, two principal constituents—the sample generator and the maintenance predictor—are designed. The maintenance predictor which is powered by the long short-term memory recurrent neural network is developed to realize the goal of predictive maintenance. The sample generator which is formulated by the physical degradation and failure model of HSR power equipment is proposed toward the goal of proactive maintenance. Test results on a gas-insulated switchgear have shown the powerful collaboration between the generator and the predictor, to not only accurately predict future maintenance timing of the switchgear based on historical sample data, but also enrich the data supply proactively to deal with potential data deficiency problems.

Conventional maintenance mode for the traction power supply system (TPSS) is to perform scheduled regular maintenance activities for power supply equipment, while such maintenance mode may result in undue maintenance tasks and low efficiency due to different degradation processes of different sorts of equipment. To address this problem, this paper introduces a preventive opportunistic maintenance (POM) method for TPSS based on equipment reliability. Firstly, a POM model is established by considering the equipment reliability degradation process based on Weibull distribution. Then, by considering the total power outage time in the planned operation cycle of TPSS as the optimization objective, the optimal maintenance scheme of TPSS is formulated by iterative method of maintenance strategies. The proposed method is verified by introducing practical maintenance strategies and fault record data of the traction transformer, circuit breaker and disconnector in an actual TPSS of a railway administration. Results show that the presented method can make full use of the existing fault data to develop a POM scheme for TPSS. It can improve maintenance efficiency and reduce power outage time, providing guidance to formulate scientific maintenance strategies for TPSS.

Aiming at wind turbines, the opportunistic maintenance optimization is carried out for multi-component system, where minimal repair, imperfect repair, replacement as well as their effects on component’s effective age are considered. At each inspection point, appropriate maintenance mode is selected according to the component’s effective age and its maintenance threshold. To utilize the maintenance opportunities for the components among the wind turbines, opportunistic maintenance approach is adopted. Meanwhile, the influence of seasonal factor on the component’s failure rate and improvement factor’s decrease with the increase of repair’s times are also taken into account. The maintenance threshold is set as the decision variable, and an opportunistic maintenance optimization model is proposed to minimize wind turbine’s life-cycle maintenance cost. Moreover, genetic algorithm is adopted to solve the model, and the effectiveness is verified with a case study. The results show that based on the component’s inherent reliability and maintainability, the proposed model can provide optimal maintenance plans accordingly. Furthermore, the higher the component’s reliability and maintainability are, the less the times of repair and replacement will be.

Continuous emission monitoring system (CEMS) is widely used in the field of environmental protection, but the maintenance routines of it consist mostly of pre-defined activities. Reliability-centered maintenance (RCM) uses a systematic approach to develop maintenance strategies that take into account factors such as system life, operating efficiency, and maintenance cost-effectiveness. This paper is motivated to apply RCM to sampling subsystem in CEMS. Based on the failure mode and effect analysis (FMEA), the RCM logic decision used to identify the reasonable maintenance mode is implemented. Subsequently, an average availability model is proposed to determine the maintenance interval cycle. Finally, the maintenance outline is formed, which can be then further used as valuable information to improve equipment reliability and develop maintenance strategies.

Deficiencies exist during applying the traditional preventive opportunistic maintenance (POM) strategy to wind turbines. For example, its rough cost analysis only provides a total instead of the staged cost of once maintenance, which cannot give precise cost composition. Setting identical thresholds for all subassemblies reduces the potential of cost saving etc. An enhanced POM strategy for wind turbines is presented in this study with the consideration of maintenance modes including no maintenance, imperfect maintenance and replacement. The reliability variations of subassemblies under different maintenance modes are described by Weibull distribution with an age reduction factor. On the basis of reliability variations, a formula is proposed to uniformly compute maintenance cost under each maintenance mode via subdividing the cost in the light of diverse maintenance stages. An integrated objective function minimising the maintenance cost is established and solved, where reliability thresholds of each subassembly are separately set as optimisation variables. The enhanced POM strategy is applied to a wind farm in North China, verifying its effectiveness.

The development of product-service-system (PSS) urges the emergence of service-oriented maintenance mode (SOMA), which leads to the shift of maintenance manners from traditional high-cost in-house maintenance to the performance-based proactive service offerings for complex multi-component systems. In this case, a novel and adaptive maintenance grouping strategy that considers service responses, service interactions and dynamic prediction requirements is desired to implement the predictive maintenance planning. Thus, this paper presents a service-oriented dynamic multi-level predictive maintenance grouping strategy. It involves component level, dynamic grouping level and system level to fulfill the individual optimization and grouping optimization. Firstly, the individual service time based on real-time remaining useful life (RUL) distribution information is optimized and obtained with the minimum average cost. Secondly, considering the existing economic and resource dependence in predictive and corrective services, this paper dynamically groups the predicted optimal individual services from rolling planning horizon at each calculated service planning time. The penalty costs and grouping service costs are both constructed. Then, a modified k-means method is designed to find out the optimal short-term grouping execution strategy from medium-term prediction information where the average cost savings and relative availability improvement degree are regarded as the optimization objectives. Finally, some numerical examples with three predictive grouping scenarios show that our grouping strategy could provide a feasible and effective long-term dynamic maintenance planning for proactive OEM or PSS providers. It is adapted to the SOMA on complex multi-component systems.

With the rapid increase in the number of power grid equipment, advanced equipment maintenance methods are necessary. Two kinds of maintenance mode, including the reliability centered maintenance (RCM) and risk based maintenance (RBM), are analyzed in this paper. On this basis, taking RBM as an example, an application example is given, which provides a reference for the evaluation of equipment condition and the improvement of maintenance methods in China's power grid.

The research improves the DC reliability management index system. The first part is the improvement and application analysis of the DC reliability assessment index. The second part is the quantitative evaluation algorithm research and software development to evaluate the impact of DC system shutdown on system reliability, including the construction of evaluation indicators and the application test of the optimal selection of DC outages. Based on the analysis results, the active planned outage model is proposed to optimize the Equipment maintenance mechanism. The research put forward the "active planned outage" maintenance mode and the "active planned energy unavailability rate" indicators and verified the feasibility of the optimized model of power outage maintenance with an example analysis. Finally, based on the two new indicators proposed by the research, combined with the development strategy and work focus of the EHV, the research proposed an optimized HVDC reliability indicator system and explained the orientation and significance of the indicator system.

With the participation of social capital, the operation and maintenance performance of Public-Private Partnership (PPP) urban infrastructure is becoming the key value-added point. This chapter summarizes and analyzes the problems of traditional urban infrastructure operation and maintenance (UIOM), and innovatively proposes the UIOM mode under the PPP background. Combined with the characteristics of the new generation of information technology, this chapter puts forward an efficient and intelligent UIOM mode with the features of intelligent decision-making, fine maintenance, technical optimization, capital saving, resource integration, and sustainability. This chapter takes the Highway Tunnel as the example, propose the Evaluation Index System for Intelligent UIOM Mode and implement the evaluation process using the AHP. The proposed intelligent UIOM Mode provides a scientific solution to the improvement of the subsequent evaluation system and the optimization of operation and maintenance decisions in PPP urban infrastructure projects.

We consider the integrated preventive maintenance and spare part ordering strategy problem for age deterioration components in which there are distinguishing critical and non-critical components. The component deterioration process is modeled as a three-stage failure process in which multiple maintenance modes are conducted to restore the machines after periodical inspection. Some spare parts are stored based on historical repair records while certain spare parts are ordered alternatively whenever necessary. The influence of spare parts ordering associated with cost and fulfillment of maintenance mode. Our objective is to minimize the expected total cost per unit time. We formulated the model based on the renew theory and characterize the scenarios of combinations of maintenance mode selection and spare parts ordering strategy. Numerical experiments of a case study showed that the cost-saving obtained by integrated optimization of maintenance mode selection and spare parts ordering decisions are significant.

Aiming at the problems in the operation and maintenance mode of coal mine reducer, such as poor real-time and flexibility, high cost, insufficient data collection. an embedded hardware platform is designed, and its working principle and hardware composition are introduced in detail. The platform collects the vibration acceleration, vibration velocity and temperature signals of coal mine decelerator, and conducts fast Fourier transform, envelope spectrum analysis and other processing to obtain the health status of each key part of coal mine decelerator. The practical application shows that the platform has stable performance, meets the requirements of field real-time analysis and flexible layout, and the prediction results have high reliability and small error.

In this paper, reliability assessment technology based on N-X service impact analysis is studied to accurately evaluate the risk value of power communication network service under N-X maintenance mode when the maintenance object is in maintenance state. On this basis, N-X service risk analysis is studied under the maintenance mode. Under the maintenance condition, the link or node equipment is determined to be in the maintenance state through maintenance, and the service route will be changed accordingly. All the link and node equipment that cannot be repaired are given to complete the maintenance task with low risk. Study the service reliability assurance technology based on N-X risk, develop the evaluation indexes related to network service protection, simulate the network fault X in the network, statistically evaluate the affected situation of the service, and carry out routing and protection upgrade and optimization for the service affected by the over-threshold or fault.

For comprehensively considering the equipment health index, maintenance cost and equipment failure loss, a decision-making model for maintenance mode selection is proposed in this paper. We define the equipment risk loss is the product of equipment failure loss and equipment failure probability. The equipment fault loss is composed of equipment asset loss and power grid operation loss, and the specific calculation formula of each part is given in this paper. The influence of the composition elements of maintenance cost, calculation methods and different maintenance methods on equipment failure rate are analyzed. The objective is minimizing the sum of maintenance cost and equipment risk loss after maintenance, a quantitative method is used to select the maintenance mode of distribution network equipment, and the maintenance mode selection process of two transformers in a certain state is given as the calculation example and the results verified the effectiveness of the proposed method.

This paper discusses research and trial application of mobile sensing and interactive diagnostic technology for power cable lines. The comprehensive construction of intelligent operation and maintenance system combined with the modern information technology, intelligent technology and the traditional technique is an urgent demand. The mobile sensing and interactive diagnostic technology for power cable lines integrates technique of conventional statue detecting, on-line monitoring, tunnel monitoring and patrol robot, supported by the internet of things technology and combined with RFID technology, individual mobile terminal technology, multiple states sensing technology, patrol robot technology, the distributed diagnosis technology and data interactive technology. The whole system makes operation staffs conveniently acquire operation indicators and preliminary analysis of power cables lines and tunnels in the process of patrol and maintenance, master and update realtime operational information of power cables lines and tunnels, such as operating state, fault prediction, risk level and maintenance recommendation by communicating with control center platform. Further application of the technology will innovate maintenance mode and promote intelligent level, efficiency and effects of operating management and maintenance of power cable lines comprehensively.

With the development of intelligent substation, the traditional operation and maintenance mode has been unable to meet the requirements of substation. The key to improve the operation and maintenance level is to realize condition monitoring and maintenance based on online operation and maintenance. How to build and realize the online operation and maintenance system of relay protection is mainly introduced in this paper. In addition, the key technologies of operation and maintenance system architecture, protection equipment condition monitoring, secondary circuit condition monitoring and auxiliary maintenance operation are introduced and discussed. Finally, the feasibility of the implementation method of the proposed online operation and maintenance is verified by practice.

The research implementation is divided into three main parts. The first part is the improvement and application analysis of the Direct Current reliability assessment index. The second part is the quantitative evaluation algorithm research and software development to evaluate the impact of the Direct Current system outage on system reliability. The third part is the application technology research of the grid reliability assessment tool in the maintenance and repair of Direct Current equipment, including the construction of evaluation indicators and the application test of the optimal selection of the Direct Current outage time. Based on the analysis results, an active planned outage mode is proposed and optimized. Equipment maintenance mechanism. The research team proposed the “active planned outage” maintenance mode and “active planned energy unavailability rate” indicators and verified the feasibility of the power outage maintenance optimization model with an example analysis. Finally, based on the three new indicators proposed by this research, combined with the development strategy, the research proposed an optimized Direct Current transmission reliability indicator system and explained the guidance and significance of the indicator system.

At present, there are no good indicators for reliability and service assessment of power transmission companies. The contradiction relationship points out that the current DC energy availability index is inadequate, and two new reliability indexes, “channel operation utilization rate” and “operation availability rate”, are proposed. At the same time, the “active planning outage” maintenance mode and “active planning energy unavailability rate” indicators were proposed, and the reliability evaluation program algorithm developed based on programming was analyzed to verify the feasibility of the power failure maintenance optimization mode. Based on the contradictory relationship between planned outage and forced outage in engineering practice, it is pointed out that the current DC energy availability index is insufficient, and two new reliability indicators, “channel operation utilization rate” and “operation availability rate”, are proposed. The practicality and effectiveness of the new index are verified by actual running data examples. The proposed “operation availability” indicator can better guide the operation and maintenance department to deal with equipment defects and hidden dangers in a timely manner and improve the reliability of the DC system operation.

The use and maintenance support requirements of modern weapons and equipment are constantly improving, and the maintenance mode of armored equipment is changing from "passive maintenance" to "active maintenance". The core and foundation of active maintenance is to master the health status of equipment operation comprehensively and accurately. This paper presents a research method of maintenance strategy of fire control system based on hierarchical state evaluation. Firstly, we evaluate the current operation state of fire control system layer by layer; and then a performance degradation and failure trend model is established by stochastic process. Secondly, through the competitive failure mechanism of soft failure due to loss and hard failure due to sudden occurrence, the reliability index that can reflect the operation state of the equipment is determined. The research on active maintenance strategy of fire control system not only improves the combat readiness of weapon system, but also ensures the best performance of fire control system with low cost and high efficiency.

Metro door system (MDS) is one of the most important and fault-prone parts of metro train. Reliability-centered maintenance (RCM) is a systematic engineering process for determining the preventive maintenance requirements of equipment and optimizing the maintenance system. This paper is motivated to apply RCM to the maintenance of metro door system. Firstly, the subsystems and components of MDS are introduced. Then a fuzzy FMECA method is proposed to define the hazard degree of 10 failure modes from 5 components with high failure rate to conduct reliability analysis. Finally, reliability-centered maintenance strategy is made after maintenance mode decision and calculation of maintenance interval cycle, which provides theoretical basis to the maintenance of the metro train.

With the rapid development of China's economy, smart grid has been built in an all-round way. As an important basic node of smart grid, the technical complexity of smart substation automation system is getting higher and higher. Once a fault occurs, if not handled in time, it will seriously affect the safety and stability of the power grid, which poses a great loss to economic production activities. The traditional mode of local maintenance has the drawbacks of high time consuming, low efficiency, and heavy workload. To solve these problems, a remote maintenance technology for substation automation equipment based on general service protocol (GSP) is first proposed in this paper, and the brand new system architecture is designed which involves the service management center, the maintenance center, and substations. The standardized interaction mechanism, integrated deep security protection methods, and flow control strategy are adopted to ensure the standardization, security, and reliability of the remote maintenance system. Finally, the effectiveness of the proposed methods is successfully demonstrated, and the comprehensive performance of the remote maintenance system is evaluated by comparing with the traditional local maintenance mode.

This study considers the decentralized maintenance of a multistate system (MSS) with low-priority components (LPCs) and high-priority components (HPCs). By introducing imperfect observations of the state, the MSS can be modeled as a partially observable Markov decision process. We propose an $(m,\,N)$ maintenance policy, where it is considered that the MSS has failed when an HPC fails or when the number of failed LPCs reaches $m$ . In contrast to a centralized maintenance mode, two maintenance teams conduct reliability evaluations and maintenance actions. One team employs the Markov method to predict the trends in the deterioration of the components. The other team estimates the status of the MSS based on the sample data, which are stochastically related to the condition of the system. The different teams may have different maintenance costs and effects, and either maintenance team can be selected based on the system's status. We discuss in detail how to arrange the maintenance teams in order to obtain the lowest expected cost rate with a guarantee of system reliability. Illustrative numerical examples are provided to show the significant cost savings under decentralized maintenance compared with centralized maintenance due to either lower expenditure or shorter time requirements.

This paper demonstrates the operation of a 1 MW/2 MWh grid-tied battery energy storage system (BESS) in a 10 MW wind R&D park for Automatic Generation Control (AGC) for 29 days. The efficiency and utilization of the BESS in the context of regulation and grid integration are examined. The response time for the BESS is as low as one second, which is faster than the current accepted practice of a conventional generator with governor control. Using PJM's performance template gives an average performance score of 93% while the storage is providing AGC. However, because the storage system only charges when there is sufficient wind energy and spent significant time in maintenance mode, the 29-day performance average is only 65%. The battery was able to carry out some mode of AGC for 64% of the test period. When energy costs and battery degradation are considered, utilizing the battery costs USD 19,000 over the 29-day period, whereas the potential income from AGC, charging only with wind power, was USD 9,037. This 29-day demonstration shows that batteries have fast response and can perform AGC, but within a wind farm AGC is unlikely to be suitable without changes in the tariff schemes.