Preventive Maintenance Problem Research Articles

Optimal replacement policy based on number of failures for a system with multiple attempt minimal repairs

This study proposes a failure number-dependent replacement policy for a minimal repairable system with multi-attempt repairs. The motivation for this study is based on the novel notion of "multiple attempts minimal repair," first introduced by Cha and Finkelstein [1,2] recently. Their repair/replacement model, along with useful properties derived and discussed for optimal preventive maintenance (PM) problems, served as the motivation for this study. This paper analyzes the optimal replacement policy based on the number of failures for a multi-attempt minimal repairable system.Our policy's characteristic is that the corrective replacement (CR) of a system is only possible at a system failure instant, rather than implementing a scheduled preventive replacement (PR). Specifically, under our policy, the system is replaced not only after a fixed number of unsuccessful repair attempts (k), but also upon reaching a predetermined failure number (n). We show that the optimal policy (or optimal n*) that minimizes the average cost per unit of time in the long run can be determined by the unique solution of an equation under certain conditions. The numerical results demonstrate that our policy is more efficient in terms of expected cost rate than other policies with the scheduled PR.

A self-adaptive co-evolutionary algorithm for multi-objective flexible job-shop rescheduling problem with multi-phase processing speed selection, condition-based preventive maintenance and dynamic repairman assignment

Production scheduling and maintenance planning are two interactive factors in modern manufacturing system. However, at present, almost all studies ignore the impact of unpunctual maintenance activities on the integrated production and maintenance scheduling since the unavailabilities of repairmen are dynamically changed, e.g., repairmen increase, decrease and their unavailable intervals update. Under these contexts, this paper addresses a novel integrated optimization problem of condition-based preventive maintenance (CBPM) and production rescheduling with multi-phase processing speed selection and dynamic repairman assignment. More precisely, (1) a novel multi-phase-multi-threshold CBPM policy with remaining-useful-life-based inspection and multi-phase processing speed selection is proposed to obtain some selectable maintenance plans for each production machine; (2) a hybrid rescheduling strategy (HRS) that includes three rescheduling strategies is designed for responding to the dynamic changes of repairman; and (3) an adaptive clustering- and Meta-Lamarckian learning-based bi-population co-evolutionary algorithm (ACML-BCEA) is developed to deal with the concerned problem. In the numerical simulations, the effectiveness of designed operators and proposed ACML-BCEA algorithm is first verified. Next, the superiority and competitiveness of the proposed CBPM policy and HRS are separately demonstrated by comparing with other CBPM policies and rescheduling strategies. After that, a comprehensive sensitivity analysis is performed to analyze the effect of optional range of processing speed, skill level of selectable repairmen and total number of processing phases, and the analyzing results show that these factors all have a significant impact on the integrated optimization.

Preventive maintenance for the constrained multi-attempt minimal repair

In practice, at some instances, the repair of a failed item can be unsuccessful and should be repeated due to various reasons. This can happen in practice with a minimal repair when a failed part of a system is replaced, e.g., by the faulty spare. The corresponding stochastic model that takes into account this possibility can be described probabilistically in terms of the Polya-Aeppli process, which is a generalization of the Poisson process to the case of multiple occurrences of events. Some useful properties for considering optimal preventive maintenance problems are derived. The optimal replacement policy for a system with the multi-attempt minimal repairs is defined and analysed. The characteristic feature of this policy is that the system is replaced not only on reaching the predetermined age but also after a fixed number of unsuccessful minimal repairs. The detailed numerical examples illustrate our findings.

A Customized IMOEA/D for Bi-Objective Single-Machine Scheduling with Adaptive Preventive Maintenance

The prolonged operation of machines in the production process can lead to continuous deterioration or even failure, and the necessary maintenance measures can alleviate the above negative effects. For this reason, this study investigates a joint optimization problem of single-machine production and preventive maintenance (PM) considering linear deterioration effects. The objective is to obtain an integrated sequence of degrading jobs and PM activities in order to simultaneously minimize the makespan and the total cost. Based on the problem characteristics, an adaptive PM strategy is first designed. To efficiently solve the problem, an improved multi-objective evolutionary algorithm based on decomposition (IMOEA/D) is tailored, where the biased-distribution weight vector is proposed to enhance the search capability at both ends of the Pareto front. Five instances are used to evaluate the performance of the customized IMOEA/D and two classical multi-objective evolutionary algorithms. Numerical studies show that the IMOEA/D can substantially improve the hypervolume metric, the maximum spread metric, and the distributivity of the Pareto front at a slight sacrifice of the spacing metric.

Experimental test of a building thermal system for preventive maintenance based on thermoeconomic analysis

An analysis of the sectorial structure of energy consumption shows that residential and tertiary sector buildings are the third-highest consumers, responsible for 29.5% of a city’s final energy consumption. The Building Quality Control Laboratory of the Basque Government aims to promote quality, innovation, and sustainability in buildings. To accomplish this goal, it has constructed an experimental facility with different energy generation technologies and a very versatile control system for testing different energy systems and operation modes. In this study, we tested a facility for supplying domestic hot water and heating for a multi-family house by means of a condensing boiler and an aerothermal heat pump (together with the corresponding control). This installation could reproduce the thermal demands required to be satisfied by the generation equipment through a programmed operation of the installation based on real demands. Additionally, this installation was analyzed using thermoeconomics (TE) to solve problems unable to be solved using traditional energy analyses based on the First Law of Thermodynamics. These problems include: (1) Determining the costs of the products of the installation based on physical criteria, (2) detecting the places where losses actually occur, evaluating their costs, and proposing cost-effective improvements, and (3) diagnosing issues in the installation. As a result, this paper suggests a solution to the preventive maintenance problems confronting the technical maintenance personnel for thermal installations in buildings by applying TE knowledge and using real data collected from sensors.

An Integrated Flow Shop Scheduling Problem of Preventive Maintenance and Degradation With an Improved NSGA-II Algorithm

The main objective of this research is to establish and solve a scheduling model for the degraded flow shop taking completion time and average device idle time as optimization objectives. Therefore, considering the interaction among schedule, production, maintenance and degradation, a mathematical model with completion time as well as average device idle time is constructed. Based on the NSGA-II algorithm, a local search strategy is first proposed to obtain junction points and sparse points according to the non-dominated sorted results. Subsequently, the comparative experiments are conducted utilizing NSGA-II-ALS, NSGA-II, and PSO algorithms. The optimal solution sets and Pareto diagrams of the three algorithms are obtained. The Gantt charts of the optimal solutions are drawn. Eventually, the multi-objective evaluation indexes of each algorithm are calculated and compared. Using statistical experiments and analysis, it can be found that NSGA-II-ALS exhibit excellent performance compared with other algorithms. The scheduling model proposed in this paper is able to effectively coordinate and balance the relationship among production, equipment maintenance and degradation, thus improving the efficiency of manufacturing systems, saving production costs, and realizing the optimal utilization of resources.

Bayesian Statistical Method Enhance the Decision-Making for Imperfect Preventive Maintenance with a Hybrid Competing Failure Mode

The study aims to provide a Bayesian statistical method with natural conjugate for facilities’ preventive maintenance scheduling related to the hybrid competing failure mode. An effective preventive maintenance strategy not only can improve a system’s health condition but also can increase a system’s efficiency, and therefore a firm needs to make an appropriate strategy for increasing the utilization of a system with reasonable costs. In the last decades, preventive maintenance issues of deteriorating systems have been studied in the related literature, and hundreds of maintenance/replacement models have been created. However, few studies focused on the issue of hybrid deteriorating systems which are composed of maintainable and non-maintainable failure modes. Moreover, due to the situations of the scarcity of historical failure data, the related analyses of preventive maintenance would be difficult to perform. Based on the above two reasons, this study proposed a Bayesian statistical method to deal with such preventive maintenance problems. Non-homogeneous Poisson processes (NHPP) with power law failure intensity functions are employed to describe the system’s deterioration behavior. Accordingly, the study can provide useful ways to help managers to make effective decisions for preventive maintenance. To apply the proposed models in actual cases, the study provides solution algorithms and a computerized architecture design for decision-makers to realize the computerization of decision-making.

Double-Layer Q-Learning-Based Joint Decision-Making of Dual Resource-Constrained Aircraft Assembly Scheduling and Flexible Preventive Maintenance

This study investigates an integrated optimization problem of aircraft assembly scheduling and flexible preventive maintenance (PM) with decision-making (FPM/DM), in which both machine flexibility and worker flexibility are considered. A mixed integer linear programming (MILP) model targeted at minimizing the assembly cycle is first established to formulate the problem. Then, CPLEX is used for solving the exact solution of the MILP model, however, it is time-consuming and difficult to apply to complex scenarios. For the tradeoff between solution quality and computational efficiency, an improved double-layer Q-learning (QL) algorithm with PM decision-making (IDLQL/PM) is further designed. Specifically, upper-layer QL is responsible for learning a proper machine selection heuristic from the given action set to ensure the machine load balance, and lower-layer QL provides a proper action (either an operation or a PM) for the selected machine to reduce unnecessary idle time and PM. Next, some instances are generated to evaluate the performance of IDLQL/PM. In a small-sized instance compared to CPLEX, the average solution gap is 1.8%, while the model solving time is reduced by 481%. As for 16 large-sized instances, IDLQL/PM presents a clear advantage over the well-known genetic algorithm and two other QL-based approaches. In addition, three state-of-the-art maintenance strategies are selected as rivals to validate the effectiveness of FPM/DM. It is observed that the proposed FPM/DM strategy can avoid improper maintenance during aircraft final assembly and ensure higher assembly efficiency.

Energy-conscious maintenance and production scheduling for single machine systems under time-of-use tariffs

In view of the joint optimization problem of preventive maintenance and production scheduling for modern production systems under time-of-use tariffs, a two-stage joint decision-making policy is proposed to achieve the peak-shifting reduction of production power. In the first stage, a dynamic preventive maintenance schedule is sequentially obtained based on the availability of machine. In the second stage, the production scheduling optimization of multi-workpiece processing is further carried out. The power consumption cost and the delay penalty cost under the time-of-use electricity tariff are considered, and the mixed integer programming model is established to achieve the balance of energy consumption and production delay. Numerical experiments have shown that by reasonably planning the idle time at the time of production batch conversion, the proposed model can effectively shift the on-peak power demand to off-peak, meet the stable electricity demand of enterprises, and improve the sustainable utilization level of power.

Joint optimisation of uncertain distributed manufacturing and preventive maintenance for semiconductor wafers considering multi-energy complementary

Aiming at the joint optimisation problem of uncertain distributed manufacturing and preventive maintenance (PM) for semiconductor wafers considering multi-energy complementary (UDMPM-MEC), a bi-level integrated scheduling model is constructed with interval makespan, total interval carbon emissions and total interval cost as the optimisation objectives. The upper model is used to obtain the joint optimisation scheduling scheme of distributed manufacturing and PM, and the lower model determines the energy allocation scheme on this basis. Given the problem characteristics, we propose an improved multi-objective whale swarm algorithm (IMOWSA). By designing jobs-to-factories allocation strategy, synchronous scheduling maintenance strategy, multi-energy complementary energy allocation decoding strategy, individual distance calculation strategy, multi-neighbourhood search strategy and deduplication strategy, the local and global search are effectively balanced, and the convergence precision is improved. A large number of simulation experiments and comparative analyses show that the IMOWSA algorithm is feasible and effective to solve this problem.

Digital twin-enabled dynamic scheduling with preventive maintenance using a double-layer Q-learning algorithm

Dynamic scheduling methods are essential and critical to manufacturing systems because of uncertain events in the production process, such as new job insertions, order cancellations, worker absences, and machine breakdowns. Emerging digital twin (DT) technology can help detect disturbances by continuously comparing physical space with virtual space and triggering a rescheduling policy immediately after a disturbance. This enables dynamic scheduling and greatly reduces the deviation between preschedules and actual schedules. This study focuses on a DT-enabled integrated optimisation problem of flexible job shop scheduling and flexible preventive maintenance (PM) considering both machine and worker resources. A double-layer Q-learning algorithm (DLQL) is designed as the underlying key optimisation method to simultaneously learn the selection process of machines and operations to achieve efficient real-time scheduling. The superior solution performance of DLQL was verified by comparing it with two well-known metaheuristic algorithms and a single-layer Q-learning algorithm under several benchmarks. Furthermore, different disturbance settings were designed to illustrate the DLQL-based dynamic scheduling process in detail. The proposed reinforcement learning (RL)-driven DT enables efficient collaborative scheduling between production and maintenance departments and helps manufacturing companies improve the real-time decision-making process under uncertain perturbations.

Data Acquisition System for Preventive Maintenance of Equipment in the Urban Tunnels

Urban Tunnel construction for supporting the Under-ground Transit Solutions (UTS) namely Metros and Subways in highly densely populated locations involves huge power requirements. This power requirement is normally met by the power supplied by the power agencies for the over the ground requirements. However, in the cases of underground work at the drilling site or de-watering sites we need to use the smaller portable generator sets. These generator sets generate a lot of harmful gases and CO2 and in-cases of over loading lead to short circuits and also fire. In the cases of prolonged tunneling times these generators remain inside the tunnel and are not serviced on time leading to gen-sets often going out of order leading to loss of work and may also cause accidents. Regular maintenance enhances the lifespan of the generators. This paper takes the approach to provide a solution to the preventive generator maintenance problems which would take care of reliability issues. In the proposed design we monitor the vital parameters of the gen-sets like fuel levels, temperatures, loads (current and voltage), running time, last servicing time, average fuel consumption, last serviced dates, battery voltages, Air filter life etc. This information is then collected by an on-board Data Acquisition System (DAS) which is then passed on to Cloud via a Wi-Fi connection. Once this information is made available on the cloud, we can apply the Machine Learning Algorithms to predict the failure rates of the generators and calculate the RUL - Remaining Useful Life.

Integrated scheduling and flexible maintenance in deteriorating multi-state single machine system using a reinforcement learning approach

This paper studies an integrated optimization problem of production scheduling and flexible preventive maintenance (PM) in a multi-state single machine system with deteriorating effects. A flexible PM strategy is proposed to proactively cope with machine failures while ensuring relatively regular PM intervals, which is composed of time-based PM (TBPM) and condition-based PM (CBPM). TBPM is conducted within every flexible time window and CBPM is implemented immediately after the most deteriorated yet still functional state. An illustrative case is presented using the enumeration approach to demonstrate the integration of production scheduling and machine maintenance. Then, Q-learning-based solution framework (QLSF) is further designed with proper state and action sets and reward functions to facilitate the determination of appropriate production scheduling rule under the constraint of the flexible maintenance. Numerical experiments show that the proposed QLSF outperforms the other four state-of-the-art scheduling rules in different scenarios. Moreover, the performance of the proposed flexible PM strategy is also examined and validated in comparison with three candidate maintenance strategies, i.e., run-to-failure corrective maintenance (CM), combination of TBPM and CM, and CBPM. The proposed flexible maintenance and solution approach can enrich the relevant academic knowledge base, and provide managerial insights and guidance in practical production systems.

Troubleshooting technological aggregates based on machine learning

The technological aggregates preventive maintenance problem being researched is resistive against a type of failures. The preventive maintenance is a cyber-production control system function and is done by activation of re-arrangement manipulation system, which is used to eliminate technological aggregates failures. The technological aggregates restoration operation technology is viewed as a man substitution in the Industry 4.0 paradigm. The equipment failure is viewed as an incident of cyber-production functional safety system, which is capable to cause a potential complex damage. The equipment robotized maintenance model is oriented to increase the cyber-production functional reliability and to use the machine learning methods to make intellectual the industrial automatics. They analyze the repair and restoration works mechanisms in the process factory level (the physical workshop) and in the analytical factory level (the virtual cloud) for a cyber-production, which interact with operation system. There is the equipment robotized maintenance algorithm proposed, which gives the data to the control system for its actual state. There is the automatic control system scheme proposed to make a technological aggregates preventive maintenance. An option how to change a failed unit is selected after the technological aggregates conditions pre-history analyzing obtained in the stage of machine learning of collected statistics.

Application of Stochastic Optimization to Optimal Preventive Maintenance Problem

The contribution deals with the optimization of a sequential preventive maintenance schedule of a technical device. We are given an initial time-to-failure probability distribution, model of changes of this distribution after maintenance actions, as well as the costs of maintenance, of a device acquisition, and of the impact of failure. The maintenance timing and, eventually, its extent, are the matter of optimization. The objective of the contribution is two-fold: first, to formulate a proper (random) objective function evaluating the lifetime of the maintained device relatively to maintenance costs; second, to propose a numerical method searching for a maintenance policy optimizing selected characteristics of this objective function. The method is based on the MCMC random search combined with simulated annealing. It is also shown that such a method is rather universal for different problem specifications. The approach will be illustrated on an artificial example dealing with accelerated lifetime after each maintenance action.

Design of an intelligent decision-making system for maintenance practices using fuzzy inference system in northern Indian SMEs

PurposeThe paper aims to analyze the significance of key maintenance practices in effective implementation of the performance parameters in Northern Indian small and medium-sized enterprises (SMEs).Design/methodology/approachThe present study deploys a fuzzy inference approach (fuzzy logic toolbox) to test the effective implementation of maintenance practices in SMEs. The significant maintenance factors are defined from applicable literature for this reason and validated by industry experts.FindingsA model built into a fuzzy rule viewer and a surface view tool built into a fuzzy toolbox in MATLAB has highlighted the successful implementation of maintenance practices in Indian SMEs. The study highlights that corrective maintenance, general maintenance, preventive maintenance and breakdown maintenance problems have emerged as important indicator variables for effective synergistic implementation of maintenance practices in northern Indian SMEs.Originality/valueAn output assessment model is developed using a fuzzy set theory for maintenance practices, where performance evaluation is significant to the overall performance of SMEs.

State-dependent age replacement policy for a system subject to cascading failures

A cascading failure is a failure in a system of interconnected parts in which the failure of a part triggers the failure of successive parts. Although some initial and empirical studies for probabilistic modeling and analysis of the cascading failures have been performed in the literature, general modeling frameworks and relevant maintenance policies have been barely reported yet. However, in this article, based on a general framework for modeling cascading failures, we study the optimal preventive maintenance problem for the system. A new state-dependent age replacement policy is developed. The comparison with the ordinary age replacement policy is also discussed.

Integrated Batch Production and Multiple Preventive Maintenance Scheduling on A Single Machine to Minimize Total Actual Flow Time

We discuss the problem of batch production and multiple preventive maintenance (PM) scheduling on a single machine. On batch production systems, there is a situation that PM activity should be executed at the time when the machine is processing the parts on a batch. Delaying that action could cause an increase in the possibility of a rejected product. However, applying the PM action would interrupt the process and also need an additional setup when starting the process again. It could impact the longer total flow time. This research proposed two algorithms (the optimal and the heuristic algorithm) which integrated both of batch production and multiple preventive maintenance schedules. The performance of the algorithm measured by minimizes the total actual flow time. Experimental results show that the effectivity of the heuristic algorithm is between 66% until 100% from the optimal algorithm.

Reward-based Monte Carlo-Bayesian reinforcement learning for cyber preventive maintenance

Abstract This article considers a preventive maintenance problem related to cyber security in universities. A Bayesian Reinforcement Learning (BRL) problem is formulated using limited data from scan results and intrusion detection system warnings. The median estimated learning time (MELT) measure is introduced to evaluate the speed at which a control system effectively eliminates parametric uncertainty and probability is concentrated on a single scenario. It is demonstrated that the Monte Carlo BRL with enhancements including Latin hypercube sampling (LHS) to generate scenarios, identical systems multi-task learning, and reward-based learning achieves shorter MELT values, i.e., “faster” learning, and improved objective values compared with alternatives in a numerical study. Rigorous results establish the optimality of the derived control strategies and the fact that optimal learning is possible under steady state assumptions. Also, the real-world case study of policies for patching Linux critical server cyber vulnerabilities generates insights including the potential to reduce expenditure per host by mandating compensating controls for critical vulnerabilities.

Minimize total tardiness and machine unavailability on single machine scheduling problem: bi-objective branch and bound algorithm

The joint production scheduling and preventive maintenance problems have recently attracted researchers’ attention given their contribution, both the production and the maintenance functions and their integration, to the firms’ efficiency. In this paper, we deal with production scheduling and preventive maintenance (PM) planning on single machine problem. The aim is to find an appropriate sequencing of production jobs and a PM planning to minimize two objectives simultaneously: total tardiness of jobs and machine unavailability. We propose a bi-objective exact algorithm, that we called BOBB, based on bi-objective branch and bound method to find the efficient set. We introduced several properties and bound sets to enhance the performance of the proposed BOBB algorithm. Furthermore, we propose a hybrid method, that we called GA-BBB, based on genetic algorithm and binary branch and bound algorithm to compute an approximate efficient set to be used as an initial upper bound set in the BOBB algorithm. An experimental study was conducted to show the efficiency of the GA-BBB and the BOBB algorithms.