Optimal maintenance of multi-component systems: a review

In this article we give an overview of the literature on multi-component maintenance optimization. We focus on work appearing since the 1991 survey "A survey of maintenance models for multi-unit systems" by Cho and Parlar. This paper builds forth on the review article by Dekker et al. (1996), which focusses on economic dependence, and the survey of maintenance policies by Wang (2002), in which some group maintenance and some opportunistic maintenance policies are considered. Our classification scheme is primarily based on the dependence between components (stochastic, structural or economic). Next, we also classify the papers on the basis of the planning aspect (short-term vs long-term), the grouping of maintenance activities (either grouping preventive or corrective maintenance, or opportunistic grouping) and the optimization approach used (heuristic, policy classes or exact algorithms). Finally, we pay attention to the applications of the models.literature review;economic dependence;failure interaction;maintenance policies;grouping maintenance;multi-component systems;opportunistic maintenance;maintencance optimization;structural dependence

Reliability Analysis And Optimal Maintenance Planning For Repairable Multi-Component Systems Subject To Dependent Competing Risks

Modern engineering systems generally consist of multiple components that interact in a complex manner. Reliability analysis of multi-component repairable systems plays a critical role for system safety and cost reduction. Establishing reliability models and scheduling optimal maintenance plans for multi-component repairable systems, however, is still a big challenge when considering the dependency of component failures. Existing models commonly make prior assumptions, without statistical verification, as to whether different component failures are independent or not. In this dissertation, data-driven systematic methodologies to characterize component failure dependency of complex systems are proposed. In CHAPTER 2, a parametric reliability model is proposed to capture the statistical dependency among different component failures under partially perfect repair assumption. Based on the proposed model, statistical hypothesis tests are developed to test the dependency of component failures. In CHAPTER 3, two reliability models for multi-component systems with dependent competing risks under imperfect assumptions are proposed, i.e., generalized dependent latent age model and copula-based trend-renewal process model. The generalized dependent latent age model generalizes the partially perfect repair model by involving the extended virtual age concept. And the copula-based trend renewal process model utilizes multiple trend functions to transform the failure times from original time domain to a transformed time domain, in which the repair conditions can be treated as partially perfect. Parameter estimation methods for both models are developed. In CHAPTER 4, based on the generalized dependent latent age model, two periodic inspection-based maintenance polices are developed for a multi-component repairable system subject to dependent competing risks. The first maintenance policy assumes all the components are restored to as good as new once a failure detected, i.e., the whole system is replaced. The second maintenance policy considers the partially perfect repair, i.e., only the failed component can be replaced after detection of failures. Both the maintenance policies are optimized with the aim to minimize the expected average maintenance cost per unit time. The developed methodologies are demonstrated by using applications of real engineering systems

A Condition-Based Maintenance Model for Assets with Accelerated Deterioration Due to Fault Propagation

Complex industrial assets such as power transformers are subject to accelerated deterioration when one of its constituent component malfunctions, affecting the condition of other components, which is a phenomenon called fault propagation. In this paper, we present a novel approach for optimizing condition-based maintenance policies for such assets by modelling their deterioration as a multiple dependent deterioration path process. The aim of the policy is to replace the malfunctioned component and mitigate accelerated deterioration at minimal impact to the business. The maintenance model provides guidance on determining inspection and maintenance strategies to optimize asset availability and operational cost.This is the author accepted manuscript. The final version is available from IEEE via http://dx.doi.org/10.1109/TR.2015.243913

Post-Sale Cost Modeling and Optimization Linking Warranty and Preventive Maintenance

Ph.DDOCTOR OF PHILOSOPH

Optimal maintenance of multi-component systems: a review

In this article we give an overview of the literature on multi-component maintenance optimization. We focus on work appearing since the 1991 survey "A survey of maintenance models for multi-unit systems" by Cho and Parlar. This paper builds forth on the review article by Dekker et al. (1996), which focusses on economic dependence, and the survey of maintenance policies by Wang (2002), in which some group maintenance and some opportunistic maintenance policies are considered. Our classification scheme is primarily based on the dependence between components (stochastic, structural or economic). Next, we also classify the papers on the basis of the planning aspect (short-term vs long-term), the grouping of maintenance activities (either grouping preventive or corrective maintenance, or opportunistic grouping) and the optimization approach used (heuristic, policy classes or exact algorithms). Finally, we pay attention to the applications of the models

Modeling Preventive Maintenance in Complex Systems

This thesis presents an explicit consideration of the impacts of modeling decisions on the resulting maintenance planning. Incomplete data is common in maintenance planning, but is rarely considered explicitly. Robust optimization aims to minimize the impact of uncertainty--here, in contrast, I show how its impact can be explicitly quantified. Doing so allows decision makers to determine whether it is worthwhile to invest in reducing uncertainty about the system or the effect of maintenance. The thesis consists of two parts. Part I uses a case study to show how incomplete data arises and how the data can be used to derive models of a system. A case study based on the US Navy\u27s DDG-51 class of ships illustrates the approach. Analysis of maintenance effort and cost against time suggests that significant effort is expended on numerous small unscheduled maintenance tasks. Some of these corrective tasks are likely the result of deferring maintenance, and, ultimately decreasing the ship reliability. I use a series of graphical tests to identify the underlying failure characteristics of the ship class. The tests suggest that the class follows a renewal process, and can be modeled as a single unit, at least in terms of predicting system lifetime. Part II considers the impact of uncertainty and modeling decisions on preventive maintenance planning. I review the literature on multi-unit maintenance and provide a conceptual discussion of the impact of deferred maintenance on single and multi-unit systems. The single-unit assumption can be used without significant loss of accuracy when modeling preventive maintenance decisions, but leads to underestimating reliability and hence ultimately performance impacts in multi-unit systems. Next, I consider the two main approaches to modeling maintenance impact, Type I and Type II Kijima models and investigate the impact of maintenance level, maintenance interval, and system quality on system lifetime. I quantify the net present value obtained of the system under different maintenance strategies and show how modeling decisions and uncertainty affect how closely the actual system and maintenance policy approach the maximum net present value. Incorrect assumptions about the impact of maintenance on system aging have the most cost, while assumptions about design quality and maintenance level have significant but smaller impact. In these cases, it is generally better to underestimate quality, and to overestimate maintenance level

Integrated Systems Health Management as an Enabler for Condition Based Maintenance and Autonomic Logistics

Health monitoring systems have demonstrated the ability to detect potential failures in components and predict how long until a critical failure is likely to occur. Implementing these systems on fielded structures, aircraft, or other vehicles is often a struggle to prove cost savings or operational improvements beyond improved safety. A system architecture to identify how the health monitoring systems are integrated into fielded aircraft is developed to assess cost, operations, maintenance, and logistics trade-spaces. The efficiency of a health monitoring system is examined for impacts to the operation of a squadron of cargo aircraft revealing sensitivity to and tolerance for false alarms as a key factor in total system performance. The research focuses on the impacts of system-wide changes to several key metrics: materiel availability, materiel reliability, ownership cost, and mean downtime. Changes to theses system-wide variables include: diagnostic and prognostic error, false alarm sensitivity, supply methods and timing, maintenance manning, and maintenance repair window. Potential cost savings in maintenance and logistics processes are identified as well as increases in operational availability. The result of this research is the development of a tool to conduct trade-space analyses on the effects of health monitoring techniques on system performance and operations and maintenance costs

On The Maintenance Modeling and Optimization of Repairable Systems: Two Different Scenarios

The use of mathematical modeling for the purpose of analyzing and optimizing the performance of repairable systems is widely studied in the literature. In this dissertation, we study two different scenarios on the maintenance modeling and optimization of repairable systems. First, we study the long-run availability of a traditional repairable system that is subjected to imperfect corrective maintenance. We use Kijima\u27s second virtual age model to describe the imperfect repair process. Because of the complexity of the underlying probability models, we use simulation modeling to estimate availability performance and meta-modeling to convert the reliability and maintainability parameters of the repairable system into an availability estimate without the simulation effort. As a last step, we add age-based, perfect preventive maintenance to our analysis. Second, we optimize a preventive maintenance policy for a two-component repairable system. When either component fails, instantaneous, minimal, and costly corrective maintenance is performed on the component. At equally-spaced, discrete points during the system\u27s useful life, the decision-maker has the option to perform instantaneous, imperfect, and costly preventive maintenance on one or both of the components, to instantaneously replace one or both of the components, or to do nothing. We use a Genetic Algorithm in an attempt to find a cost-optimal set of preventive maintenance and replacement decisions