Statistically Planned and Individually Improved Predictive Maintenance Management for Continuously Monitored Degrading Systems

Purpose Making decisions on preventive maintenance (PM) policy and buffer sizing, as is often studied, may not result in overall optimization. The purpose of this paper is to propose a joint model that integrates PM and buffer sizing with consideration of quality loss for a degenerating system, which aims to minimize the average operation cost for a finite horizon. The opportunistic maintenance (OM) policy which could increase the output and decrease the cost of the system is also explored. Design/methodology/approach A joint PM and buffer size model considering quality loss is proposed. In this model, the time-based PM and the condition-based PM are taken on the upstream and the downstream machine, respectively. Further, the OM policy based on the theory of constraints (TOC) is also considered. An iterative search algorithm with Monte Carlo is developed to solve the non-linear model. A case study is conducted to illustrate the performance of the proposed PM policies. Findings The superiority of the proposed integrated policies compared with the separate PM policy is demonstrated. Effects of the policies are testified. The advantages of the proposed TOC-based OM policy is highlighted in terms of low-cost and high-output. Originality/value Few studies have been carried out to integrate decisions on PM and buffer size when taking the quality loss into consideration for degenerating systems. Most PM models treat machines equally ignoring the various roles of them. A more comprehensive and integrated model based on TOC is proposed, accompanied by an iterative search algorithm with Monte Carlo for solving it. An OM policy to further improve the performance of system is also presented.

Optimal preventive maintenance policy for leased equipment using failure rate reduction

ABSTRACTA framework for reliability and maintenance analysis of job shop manufacturing systems is proposed in this paper. An efficient preventive maintenance (PM) policy in terms of failure effects analysis (FEA) is proposed. Subsequently, reliability evaluation and component importance measure based on FEA are performed under the PM policy. A job shop manufacturing system is applied to validate the reliability evaluation and dynamic maintenance policy. Obtained results are compared with existed methods and the effectiveness is validated. Some vague understandings for issues such as network modelling, vulnerabilities identification, the evaluation criteria of repairable systems, as well as PM policy during manufacturing system reliability analysis are elaborated. This framework can help for reliability optimisation and rational maintenance resources allocation of job shop manufacturing systems.

A special game against nature is examined in this paper, in which nature controls timings of repairable and non-repairable failures of a system of equipment, and preventive maintenance, repairs and preventive replacement policies are the countermeasures. We optimize the imperfect preventive maintenance policy for a multi-unit system with different initial virtual age units. We develop a binary integer programming problem, where the management decides on the optimal preventive maintenance policy to minimize the total expected maintenance cost not to exceed a given budget. The mathematical formulation is developed for a multi-unit system, considering different preventive maintenance levels for each unit. Numerical examples with sensitivity analysis are performed to illustrate the performance and efficiency of the proposed model. Also, the model is examined for a real case study from railway industry. The results determine the optimal preventive maintenance policy and provide managerial insights based on computational analysis.

PurposeWhile steady-state analysis is useful, it does not consider the inherent transient characteristics of repairable systems' behavior, especially in systems that have relatively short life spans, or when their transient behavior is of special concern such as the motivating example used in this paper, military systems. Therefore, a maintenance policy that considers both transient and steady-state availability and aims to achieve the best trade-off between high steady-state availability and rapid stabilization is essential.Design/methodology/approachThis paper studies the transient behavior of system availability under the Kijima Type II virtual age model. While such systems achieve steady-state availability, and it has been proved that deploying preventive maintenance (PM) can significantly improve its steady-state availability, this improvement often comes at the price of longer and increased fluctuating transient behavior, which affects overall system performance. The authors present a methodology that identifies the optimal PM policy that achieves the best trade-off between high steady-state availability and rapid stabilization based on cost-availability analysis.FindingsWhen the proposed simulation-based optimization and cost analysis methodology is applied to the motivating example, it produces an optimal PM policy that achieves an availability–variability balance between transient and steady-state system behaviors. The optimal PM policy produces a notably lower availability coefficient of variation (by 11.5%), while at the same time suffering a negligible limiting availability loss of only 0.3%. The new optimal PM policy also provides cost savings of about 5% in total maintenance cost. The performed sensitivity analysis shows that the system's optimal maintenance cost is sensitive to the repair time, the shape parameter of the Weibull distribution and the downtime cost, but is robust with respect to changes in the remaining parameters.Originality/valueMost of the current maintenance models emphasize the steady-state behavior of availability and neglect its transient behavior. For some systems, using steady-state availability as the sole metric for performance is not adequate, especially in systems that have relatively short life spans or when their transient behavior affects the overall performance. However, little work has been done on the transient analysis of such systems. In this paper, the authors aim to fill this gap by emphasizing such systems and applications where transient behavior is of critical importance to efficiently optimize system performance. The authors use military systems as a motivating example.

PurposeThe purpose of this paper is to propose a predictive maintenance (PdM) system for hybrid degradation processes with continuous degradation and sudden damage to improve maintenance effectiveness.Design/methodology/approachThe PdM system updates the degradation model using partial condition monitoring information based on degradation type judgment. In addition, an extended multi-step-ahead updating stopping condition is adopted for performance enhancement of the PdM system.FindingsAn extensive numerical investigation compares the performance of the PdM system with the corresponding preventive maintenance (PM) policy. By carefully choosing the updating stopping condition, the PdM policy performs better than the corresponding PM policy.Research limitations/implicationsThe proposed PdM system is applicable to single-unit systems. And the continuous degradation process should be well modeled by the stochastic linear degradation model (Gebraeel et al., 2009).Originality/valueIn literature, there are abundant studies on PdM policies for continuous degradation processes. However, research on hybrid degradation processes still focuses on condition-based maintenance policy and a PdM policy for a hybrid degradation process is still unreported. In this paper, a PdM system for hybrid degradation processes with continuous degradation and sudden damage is proposed. The PdM system decides PM schedules by fully utilizing the condition monitoring data of each specific product, and can hopefully improve maintenance effectiveness.

Preventive maintenance (PM) is a maintenance program with activities initiated at predetermined intervals, or according to prescribed criteria, and intended to reduce the probability of failure, or the degradation of the functioning of an item. In the literature, a number of PM models have been introduced to depict the effectiveness of PM. Based on these models, approaches to scheduling PM policies have been considerably studied. This paper attempts to review existing PM models, and investigate their inter-relationships. We then categorize these models into three classes: linear, nonlinear, and a hybrid of both. These three PM model classes depict the relationships of the hazard functions before, and after a PM. Possible extensions to these three PM models are discussed. The statistical properties for models are derived, and approaches to optimizing the PM policy are given.

Deep reinforcement learning based preventive maintenance policy for serial production lines

Most preventive maintenance (PM) models require, as inputs, information on the behaviour of the equipment under failure-only maintenance (FM). Once a schedule of PM has been applied, data arising from failures are affected by the PM. Over life the behaviour that would occur under FM changes. But PM also tends to delay aging processes. This paper examines: 1) how data collected under four kinds of PM policy can be modified to re-assess the FM characteristics, and 2) how to tell whether an apparent change in them is important. The approach takes account of engineering as well as statistical factors. It concludes that: 1. Estimation of distributions and costs as they would be under FM from data collected under PM cannot be accurate but a suboptimal policy can be worse. 2. A different type of PM policy might be required following changes to costs and/or distribution. The optimum under one model might not be the overall optimum. 3. The solutions suggested in the paper remain unproved due to the ``data problem''. Mathematical research alone can never produce workable procedures. A viable methodology will need years of experimentation with real systems. 4. The potential savings are huge.

Simultaneous control of production, preventive and corrective maintenance rates of a failure-prone manufacturing system

In recent decades, philosophy behind maintenance has varied consistently due to the changes in complexity of designs, advances in automation and mechanization, adaptation to the fast growing market demand, commercial computation in the sectors, and environmental issues. In mid-forties, simplicity of designs, limited maintenance opportunities, and immaturity of trade culture made enough to perform only fix it when it broke approach, i.e. corrective maintenance, after failures. Last quarter of the 21th century made essential to constitute more conservative and preventive maintenance policies in order to ensure safety, reliability, and availability of systems with longer lifetime and cost effectiveness. Preventive maintenance can provide an economic saving more than 18% of operating cost of systems. In this basis, various stochastic models were proposed as a tool to constitute a maintenance policy to measure system availability and to obtain optimal maintenance periods. This paper presents a general perspective on common stochastic models in maintenance planning such as Homogenous Poisson Process, Non-Homogenous Poisson Process, and Imperfect Maintenance. The paper also introduces two common maintenance policies, block and age replacement policy, using these stochastic models.

In this chapter maintenance concept is defined and maintenance types are classified with regard to implementing maintenance policies toward preventive maintenance. Especially achieving planned maintenance policies toward preventive (planned) maintenance and condition based maintenance policies toward predictive maintenance is taken into consideration primarily. Maintenance concept is defined and maintenance types are classified. Due care is given for ‘Preventive Maintenance’ in this chapter. In general ‘Maintenance’ term could be defined as; The integration of all possible technical and administrative actions, including planning, supervising, monitoring and controlling toward retaining an item, a system, a machine to restore their original functional state in which they can perform the intended functions. In addition maintenance, include protective and corrective actions to keep the plant operational system in intended conditions or to maintain the acceptable manufacturing conditions. Optimum maintenance policies aims to sustain system reliability and robustness within minimum cost. In line with the progress of industry, increase in the system, material and manpower costs, increasing demand for robustness and the complex structure of the machines increases also the importance of maintenance policies. Maintenance types could be divided into two main parts namely; 1. Preventive Maintenance, 2. Corrective Maintenance. Preventive Maintenance is also classified into following sub groups; 1.a- Planned Maintenance, 1.b- Predictive Maintenance, 1.c- Advanced Maintenance Implementations; 1.c.1 Reliability Centered Maintenance 1.c.2 Risk Based Maintenance. All of these maintenance types elaborated with relevant figures in the chapter. In this chapter Comparison of Planned and Unplanned (corrective) Maintenance (With regard to transaction and output) is defined with a table. Additionally, Comparison of Planned and Unplanned (corrective) Maintenance (With regard to infra structure) is also tabulated. ‘Benefits of Preventive & Predictive Maintenance’ and ‘Predictive Maintenance Methods’ are defined with relevant descriptive figures in the chapter. For Corrective Maintenance, basic definitions and corrective maintenance steps, types of corrective Maintenance, improvement strategies in corrective maintenance effectiveness are also given. In the final part of ‘Results and conclusions’ expected and verified benefits of implementing maintenance policies for planned and predictive maintenance are explained. Comparisons in some maintenance policies is given.

Different type of maintenance problems have been investigated during the past decades. With the growth in complexity of modern systems, the cost of purchasing new systems becomes higher ever than before. Surprisingly, very little attention in the literature has been paid to the failure replacement in these preventive maintenance models. However, in practice most systems incur increasingly expensive down time costs when the systems suffer the unexpected failures. The major changes to the standard maintenance model in this research are the addition of a failure replacement cost for a failed system and the reliability limit for which system has to be replaced. The overall objective of this research is to propose an efficient algorithm to obtain the optimal maintenance policy. The optimal preventive maintenance policy is investigated with incorporating the costs of minimal repair, imperfect preventive maintenance, failure replacement and preventive replacement. The specific maintenance actions for a repairable system are when to perform an imperfect preventive maintenance or to replace the system while the system is in the operating condition. In another case, the specific maintenance actions for a repairable system are whether to replace the system, or to perform a minimal repair when the system fails. The number of imperfect preventive maintenance and the control limits of the optimal maintenance policy are calculated under the expected average cost rate criterion.

An Efficient Jaya Algorithm for Joint Optimization of Preventive Maintenance and Quality Policy in Production Systems

With the development of the wind turbine industry, the operation and maintenance of wind turbines has gradually become one of the main concerns of the wind turbine industry. In view of the wind turbine system’s vulnerability to external environment, this paper considers the performance degradation and external shocks of the unit to establish a reliability model and develop preventive maintenance policy. First, Markov chain is used for degradation modeling, and unit is divided into different health states; Homogeneous Poisson process is used for shock modeling, assuming that the shock is cumulative. The impact of shocks on unit degradation was described as the unit in different states had different state transition matrices, and then establish the state reliability. According to the state probability of the unit, formulate the corresponding preventive maintenance policy. Finally, a generator in a wind turbine system is taken as an example, the influence of external shocks on unit performance degradation is explained. Meanwhile, the preventive maintenance strategy proposed in this paper is compared with the maintenance policy with fixed maintenance threshold, and the advantages of the reliability model and maintenance strategy proposed in this paper are illustrated.