Imperfect Maintenance Research Articles

Research on Spare Part Requirement for Repairable System Based on the Quasi-Renewal Model

The spare part requirement forecast is the base of maintenance management. The exact requirement forecast an important approach to improve availability and safety of the equipments and reducing the life cycle cost of equipments. The spare part requirement are not exactly forecasted under the traditional renewal suppose, hence it easily cause waste of money and backlog of goods. To improve the ability of repairable system spare part supporting under the imperfect maintenance, this paper researches on the spare part requirement forecast for the repairable systems under imperfect maintenance, provides the spare part requirement forecast models based on quasi-renewal theory and calculates the lower and upper bounds of systems availability. Finally, an example reveals the efficiency and operability of the model.

Optimally maintaining a multi-state system with limited imperfect preventive repairs

In this paper, the optimal maintenance policy for a multi-state system with no observation is considered. Different from most existing works, only a limited number of imperfect preventive maintenance actions can be performed between two successive replacements. Assume that the system's deterioration state cannot be observed during its operation expected after each replacement, and it evolves as a discrete-time Markov chain with a finite state space. After choosing the information state as state variable, the problem is then formulated as a Markov decision process over the infinite time horizon. In order to increase the computational efficiency, several key structural properties are developed by minimising the total expected cost per unit time. The existence of the optimal threshold-type maintenance policy is proved and the monotonicity of the threshold is obtained. Finally, a numerical example is given to illustrate the optimal policy.

Fault Sampling of Complex System Under Imperfect Maintenance

When conducting simulation for evaluating complex system reliability or availability, stochastic fault sample method is applied to simulate fault occasions of complex system. Current research of fault sample method generally assumes maintenance activity restores systems to â??good-as-newâ? and â??bad-as-oldâ?, however, system usually keeps its function through imperfect maintenance that restores system to â??partly goodâ? in reality. A stochastic fault sample method for availability evaluation for complex system under imperfect maintenance based on monte-carlo simulation principle is proposed. Firstly, single component fault occurrence process subjected to imperfect maintenance is analyzed and the formula for fault occurrence occasion is given. Secondly, fault sample becomes complicated when maintenance object converts to system with complex reliability structure. Not only fault occurrence occasions but also fault components in the system should be derived from the sample method. Fault sample for parallel system is especially difficult, so the Markov state transition process is embedded into the method to solve this problem .Finally, a numerical case using fault sampling method under imperfect maintenance is given and the number of faults and their occurrence time are obtained . Validity and feasibility of the proposed method is verified.

A Joint Redundancy and Imperfect Maintenance Strategy Optimization for Multi-State Systems

The redundancy allocation problem has been extensively studied with the aim of determining optimal redundancy levels of components at various stages to achieve the required system reliability or availability. In most existing studies, failed elements are assumed to be as good as new after repair, from a failure perspective. Due to deterioration, the repaired element cannot always be restored to a virtually new condition unless replaced with a new element. In this paper, we present an approach of joint redundancy and imperfect maintenance strategy optimization for multi-state systems. Along with determining the optimal redundancy levels, the element replacement strategy under imperfect repair is also optimized simultaneously, so as to reach the desired availability with minimal average expenditure. A generalized imperfect repair model is proposed to characterize the stochastic behavior of multi-state elements (MSEs) after repair, and a replacement policy under which a MSE is replaced once it reaches the pre-determined number of failures is introduced. The cost-repair efficiency relation, which regards the imperfect repair efficiency as a function of assigned repair cost, is put forth to provide a flexibility of assigning repair efforts strategically among MSEs. The benefits of the proposed method compared to the existing ones are demonstrated and verified via an illustrative case study of a three-stage coal transportation system.

Lot sizing in a deteriorating production system under inspections, imperfect maintenance and reworks

The paper studies the joint effect of shift, inspections, imperfect preventive maintenance (PM) and imperfect rework of defective items on optimal decisions for a deteriorating production system. At the time of inspection during a production run, either PM or restoration is done depending on the state of the production process. There is a probability that PM action may shift the process from ‘in-control’ state to ‘out-of-control’ state. During the ‘out-of-control’ phase, the system produces some defective items which go for rework at the end of the production run. A portion of the reworked items may also fail repairing. The model is formulated for the case of general inspections and analyzed under two well known inspection policies—periodic inspection policy and constant cumulative hazard inspection policy. For numerical examples, a comparison of the outcomes of the model with and without reworks under these two inspection policies is made. It is observed that the periodic inspection policy performs better than the constant cumulative hazard policy.

Generalized Random Sign and Alert Delay models for imperfect maintenance

This paper considers the modelling of the process of Corrective and condition-based Preventive Maintenance, for complex repairable systems. In order to take into account the dependency between both types of maintenance and the possibility of imperfect maintenance, Generalized Competing Risks models have been introduced in "Doyen and Gaudoin (J Appl Probab 43:825-839, 2006)". In this paper, we study two classes of these models, the Generalized Random Sign and Generalized Alert Delay models. A Generalized Competing Risks model can be built as a generalization of a particular Usual Competing Risks model, either by using a virtual age framework or not. The models properties are studied and their parameterizations are discussed. Finally, simulation results and an application to real data are presented.

Decision support for life extension of technical systems through virtual age modelling

This article presents a virtual age model for decision support regarding life extension of ageing repairable systems. The aim of the model is to evaluate different life extension decision alternatives and their impact on the future performance of the system. The model can be applied to systems operated continuously (e.g., process systems) and systems operated on demand (e.g., safety systems). Deterioration and efficiency of imperfect maintenance is assessed when there is limited or no degradation data, and only failure and maintenance data is available. Systems that are in operation can be studied, meaning that the systems may be degraded. The current degradation is represented by a “current virtual age”, which is calculated from recorded maintenance data. The model parameters are estimated with the maximum likelihood method. A case study illustrates the application of the model for life extension of two fire water pumps in an oil and gas facility. The performance of the pump system is assessed with respect to number of failures, safety unavailability and costs during the life extension period.

An Opportunistic Maintenance Policy of Multi-unit Series Production System with Consideration of Imperfect Maintenance

This paper proposes an opportunistic maintenance policy for the multi-unit series production system. This proposed maintenance policy is on the basis of the failure rate analysis, which is more realistic than age-based or time-based opportunistic replacement models. When a system halts for performing preventive maintenance or opportunistic maintenance, the alternative decision of replacement and repair is made by economic evaluation of these two types of maintenance actions. The optimal preventive maintenance plans in a finite time horizon can be obtained by adjusting the preventive maintenance threshold and opportunistic maintenance threshold. A numerical example is given for illustrating the applications of the proposed policy.

Optimizing Bi-Objective Imperfect Preventive Maintenance Model for Multi-Component System Using a Proposed Improved PSO Algorithm

This study aims to establish a bi-objective imperfect preventive maintenance (BOIPM) model in which the total maintenance cost and the mean system reliability are optimized by determining the maintenance periods and maintenance activities simultaneously. To efficiently solve the established BOIPM model, this study proposes an improved particle swarm optimization (IPSO) algorithm. The IPSO extends the practicability of the conventional PSO originally designed to solve an optimization problem with continuous decision variables. Furthermore, time-varying mechanisms associated with search parameters of the PSO are utilized to enhance the particles search capability. An adjustment mechanism addressing the issue of particles falling into the infeasible area is constructed to enhance the exploring ability of the IPSO. A case verifies the effectiveness of the proposed approach.

Optimal maintenance strategy of deteriorating system under imperfect maintenance and inspection using mixed inspectionscheduling

An operating system suffers from degradation. Each degradation level can be represented by a state, making the system a multi-state system. In many situations, there are no apparent symptoms indicating the system's state, and system's degradation level can only be known through thorough inspection. Through condition monitoring, the system's state can be estimated with some uncertainty. In this work, we investigated inspection-maintenance schemes for such a system. Our assumptions are that the maintenance is imperfect and the degradation is a continuous-time Markov process. We proposed a strategy that combines both inspection and continuous monitoring to reduce unnecessary thorough inspection and to improve the system's reliability. Optimal maintenance strategy is derived based on an iterative algorithm to minimize the mean long-run costrate.

Condition-Based Maintenance with both Perfect and Imperfect Maintenance Actions


 
 
 This paper deals with a condition-based maintenance (CBM) model considering both perfect and imperfect maintenance actions for a deteriorating system whose condition is aperiod- ically monitored according to a remaining useful life (RUL) based-inspection policy. Perfect maintenance actions restore completely the system to the ’as good as new’ state. Their related cost are however often high. Imperfect preventive maintenance restores partially the system with reduced main- tenance cost. Nevertheless, it may however make the system more susceptible to future deterioration. The aim of the pa- per is to propose a CBM model which can help to construct optimal maintenance policies when both perfect and imper- fect maintenance actions are possible. To illustrate the use of the proposed CBM model, a numerical example finally is introduced.
 
 

Optimal imperfect preventive maintenance policies for a used system

In some practical situations, it may be more economical to work a used system than do a new one. From this viewpoint, this article considers three basic preventive maintenance (PM) policies for a used system: the system with initial variable damage Y 0 begins to operate at time 0, and suffers damage due to shocks. It fails when the total damage exceeds a failure level K and corrective maintenance is made immediately. To prevent such failure, it undergoes PM at a planned time T, a shock number N and a damage level k, but maintenances are imperfect. However, failure rate of a used system maybe higher than that of a new one, so some maintenance is applied to the policies at each shock in the extended models. Using the theory of cumulative processes, expected cost rate models are obtained, optimal policies which minimise them are derived analytically and discussed numerically.

Optimizing maintenance service contracts under imperfect maintenance and a finite time horizon

When a company decides to outsource a service, the most important reasons for doing so usually are to focus on core business, to be able to access high‐quality services at lower costs, or to benefit from risk sharing. However, service contracts typically follow a structure whereby both owner and contractor attempt to maximize expected profits in a noncoordinated way. Previous research has considered supply chain coordination by means of contracts but is based on unrealistic assumptions such as perfect maintenance and infinite time‐span contracts. In this work, these limitations are overcome by defining the supply chain through a preventive maintenance strategy that maximizes the total expected profit for both parties in a finite time‐span contract. This paper presents a model to establish such conditions when maintenance is imperfect, and the contract duration is fixed through a number of preventive maintenance actions along a significant part of the asset life cycle under consideration. This formulation leads to a win–win coordination under a set of restrictions that can be evaluated a priori. The proposed contract conditions motivate stakeholders to continually improve their maintenance services to reach channel coordination in which both parties obtain higher rewards. Copyright © 2012 John Wiley & Sons, Ltd.

Generalization of the proportional hazards model for maintenance modelling and optimization

A non-observable monotonically deteriorating system is considered. The observed data are the maintenance times and the kinds of maintenance performed. The aim of this paper is to propose a model of the dependency between all kinds of maintenance by considering imperfect maintenance efficiency. Furthermore, we present an additional planned preventive maintenance policy that reduces a finite horizon average total maintenance cost. To deal with such a problem a competing risks model is considered. A general class of models, denoted generalized proportional hazards, is presented and a particular model is derived. An estimation method is detailed for the proposed model. Finally, by numerical implementation, a maintenance policy that leads to the lowest finite horizon average total maintenance cost is proposed.

An Extended Sequential Imperfect Preventive Maintenance Model with Improvement Factors

This article presents a generalization of the imperfect sequential preventive maintenance (PM) policy with minimal repair. As failures occur, the system experiences one of two types of failures: a Type-I failure (minor), rectified by a minimal repair; or a Type-II failure (catastrophic) that calls for an unplanned maintenance. In each maintenance period, the system is maintained following the occurrence of a Type-II failure or at age, whichever takes place first. At the Nth maintenance, the system is replaced rather than maintained. The imperfect PM model adopted in this study incorporates with improvement factors in the hazard-rate function. Taking age-dependent minimal repair costs into consideration, the objective consists of finding the optimal PM and replacement schedule that minimize the expected cost per unit time over an infinite time-horizon.

Reliability analysis and joint assessment of Brown–Proschan preventive maintenance efficiency and intrinsic wear-out

An imperfect maintenance model for repairable systems is considered. Corrective Maintenances (CM) are assumed to be minimal, i.e. As Bad As Old (ABAO). They are considered to be left and right censored. Preventive Maintenances (PM) are assumed to be done at predetermined planned times and to follow a Brown–Proschan (BP) model, i.e. they renew (As Good As New, AGAN) the system with probability p, and they are minimal (ABAO) with probability 1−p. BP PM effects (AGAN or ABAO) are assumed to be not observed. In this context, different methods (maximum likelihood, moment estimation, and expectation–maximization algorithm) are considered in order to estimate jointly the PM efficiency parameter p and the parameters of the first time to failure distribution corresponding to the new unmaintained system. A method to individually assess the effect of each PM is also proposed. Finally, some reliability characteristics are computed: failure and cumulative failure intensities, reliability and expected cumulative number of failures. All the corresponding algorithms are detailed and applied to a real maintenance data set from an electricity power plant.

Opportunistic maintenance for wind farms considering multi-level imperfect maintenance thresholds

Abstract Few methods are available for optimizing corrective maintenance and time-based maintenance for wind farms, although these strategies are currently widely used in practice. Economic dependencies exist among wind turbine systems and their components in the wind farm. That is, it may be more economical to maintain multiple turbines or turbine components when a corrective or preventive maintenance opportunity presents. In this paper, opportunistic maintenance approaches are developed for wind farms to take advantage of the maintenance opportunities. Imperfect maintenance actions are considered, which addresses the practical issue that preventive maintenance does not always return components to as-good-as-new status. The proposed opportunistic maintenance policies are defined by the component's age threshold values, and different imperfect maintenance thresholds are introduced for failure turbines and working turbines. Three types of preventive maintenance actions are considered, including perfect, imperfect and two-level action. Simulation methods are developed to evaluate the costs of proposed opportunistic maintenance policies. Numerical examples are provided to illustrate the proposed approaches. Comparative study with the widely used corrective maintenance policy demonstrates the advantage of the proposed opportunistic maintenance methods in significantly reducing the maintenance cost. The developed methods are expected to bring immediate benefits to wind power industry.

A Data-Driven Approach to Selecting Imperfect Maintenance Models

Many imperfect maintenance models have been developed to mathematically characterize the efficiency of maintenance activity from various points of view. However, the adequacy of an imperfect maintenance model must be validated before it is used in decision making. The most adequate imperfect maintenance model among the candidates to facilitate decision making is also desired.

Collaborative optimization of maintenance and spare ordering of continuously degrading systems

A collaborative optimization model for maintenance and spare ordering of a single-unit degrading system is proposed in this paper based on the continuous detection. A gamma distribution is used to model the material degradation. The degrading decrement after the imperfect maintenance action is assumed as a random variable normal distribution. This model aims to obtain the optimal maintenance policy and spare ordering point with the expected cost rate within system lifecycle as the optimization objective. The rationality and feasibility of the model are proved through a numerical example.

Modeling and optimizing maintenance schedule for energy systems subject to degradation

In recent years, with the increasing investment on distributed energy system (DES), maintenance management has played an important role in improving the system performance. This paper aims to integrate the conception of the multiple attribute value theory and imperfect maintenance into an improved sequential preventive maintenance (PM) model for the DES subject to degradation. A model-iteration algorithm using a multi-attribute model (MAM) is developed to optimize the maintenance schedule cycle by cycle. The optimal PM interval for each cycle is obtained through MAM, by maximizing the equipment availability and minimizing the maintenance cost simultaneously. A case study is investigated to show that this sequential PM model is much more significant than conventional PM models. Finally, the sensitivity of the proposed method is investigated.