Imperfect Maintenance Research Articles

Parameter Estimation of a Reliability Model of Demand-Caused and Standby-Related Failures of Safety Components Exposed to Degradation by Demand Stress and Ageing That Undergo Imperfect Maintenance

One can find many reliability, availability, and maintainability (RAM) models proposed in the literature. However, such models become more complex day after day, as there is an attempt to capture equipment performance in a more realistic way, such as, explicitly addressing the effect of component ageing and degradation, surveillance activities, and corrective and preventive maintenance policies. Then, there is a need to fit the best model to real data by estimating the model parameters using an appropriate tool. This problem is not easy to solve in some cases since the number of parameters is large and the available data is scarce. This paper considers two main failure models commonly adopted to represent the probability of failure on demand (PFD) of safety equipment: (1) by demand-caused and (2) standby-related failures. It proposes a maximum likelihood estimation (MLE) approach for parameter estimation of a reliability model of demand-caused and standby-related failures of safety components exposed to degradation by demand stress and ageing that undergo imperfect maintenance. The case study considers real failure, test, and maintenance data for a typical motor-operated valve in a nuclear power plant. The results of the parameters estimation and the adoption of the best model are discussed.

Impact analysis of human factors on power system operation reliability

Along with the improvement of electrical equipment reliability, people’s unsafe behaviors and human errors have become one of main sources of risks in power systems. However, there is no comprehensive study on human factors and human reliability analysis in power systems. In allusion to this situation, this paper attempts to analyze the impact of human factors on power system reliability. First, this paper introduces current situation of human factors in power systems and the latest research progress in this field. Several analysis methods are proposed according to specified situations, and these methods are verified by some power system practical cases. On this base, this paper illustrates how human factors affect power system operation reliability from 2 typical aspects: imperfect maintenance caused by human errors, and impact of human factors on emergency dispatch operation and power system cascading failure. Finally, based on information decision and action in crew (IDAC), a novel dispatcher training evaluation simulation system (DTESS) is established, which can incorporate all influencing factors. Once fully developed, DTESS can be used to simulate dispatchers’ response when encountering an initial event, and improve power system dispatching reliability.

Opportunistic maintenance for wind turbines considering imperfect, reliability-based maintenance

Corrective maintenance and time-based maintenance have been widely employed in wind farms. However, the actual maintenance strategy is much more complex and diverse, and also maintenance actions do not always return the system to “as good as new” condition. Moreover, dependencies exist in multi-component systems, and it is more economical to perform preventive maintenance whenever a repair opportunity presents itself. For an opportunistic maintenance strategy, when the system is shut down for maintenance, the maintenance time of other still functioning parts can be moved up to take advantage of the huge downtime losses. In this paper, an opportunistic maintenance approach is proposed for wind turbines considering an imperfect maintenance schedule that is based on reliability. A hybrid hazard rate model is used to describe imperfect maintenance and explain the effects of corrective maintenance action. Comparative examples are used to illustrate the economic advantages of opportunistic maintenance. Simulation results demonstrate the impact that various maintenance costs (fixed downtime cost and preventive replacement cost) have on the economic benefit of opportunistic maintenance strategy. The simulation analysis also provides the optimal opportunistic maintenance reliability threshold using the fruit fly optimization algorithm to provide guidance for the actual operation and maintenance of wind farms.

An imperfect maintenance policy for mission-oriented systems subject to degradation and external shocks

This paper develops a maintenance model for mission-oriented systems subject to natural degradation and external shocks. For mission-oriented systems which are used to perform safety-critical tasks, maintenance actions need to satisfy a range of constraints such as availability/reliability, maintenance duration and the opportunity of maintenance. Additionally, in developing maintenance policy, one needs to consider the natural degradation due to aging and wearing along with the external shocks due to variations of the operating environment. In this paper, the natural degradation is modeled as a Wiener process and the arrival of random shock as a homogeneous Poisson process. The damage caused by shocks is integrated into the degradation process, according to the cumulative shock model. Improvement factor model is used to characterize the impact of maintenance actions on system restoration. Optimal maintenance policy is obtained by minimizing the long-run cost rate. Finally, an example of subsea blowout preventer system is presented to illustrate the effectiveness of the proposed model.

Reliability Assessment of Degradable Systems under Imperfect Maintenance and Utilisation Rate: A Case Study

Medical equipment is the biggest capital investment of every healthcare industry and ensuring the reliability and maintenance for critical devices is vital for Patient/user safety and better availability of services. To keep the medical device in a good operational condition, Inspection based maintenance activities have been the most usual means. The purpose of this study is to establish an improved delay time framework to model the two-stage failure process taking into account the influence of the utilization rate on the system’s degradation on the assumption of imperfect maintenance at inspection. This proportional delay time model is seldom considered in DTMs widely used in literature. A complete framework, for Parameters estimation method and the test for goodness of fit is given. To illustrate the model capabilities, a reel case study from the healthcare domain is presented, the model parameters are estimated entirely using the collected maintenance data. Then, the maximum likelihood estimation of the reliability parameters is achieved by Genetic Algorithms.

Parametric Bootstrap Goodness-of-Fit Tests for Imperfect Maintenance Models

The simultaneous modeling of ageing and maintenance efficiency of repairable systems is a major issue in reliability. Many imperfect maintenance models have been proposed. To analyze a dataset, it is necessary to check whether these models are adapted or not. In this paper, we propose a general methodology for testing the goodness of fit of any kind of imperfect maintenance model. Two families of tests are presented, based respectively on martingale residuals and probability integral transforms. The quantiles of the test statistics distributions under the null hypothesis are computed with parametric bootstrap methods. An extensive simulation study is provided, from which we recommend the use of two tests in practice, one from each family. Finally, the tests are applied to several real datasets.

Optimum Maintenance Strategy of a Repairable System Under Long-Term Free Preventive Maintenance Warranty with Predicted Maintenance

In this paper, the optimum user’s maintenance strategy of a repairable system for free preventive maintenance (PM) warranty policy is proposed. This study considers predicted maintenance due to the system failure is likely occurring and requires repair during periodic maintenance time. Periodic maintenance can be classified as one of three types — imperfect PM, perfect PM and predicted maintenance. The probability that periodic maintenance is perfect PM or predicted maintenance depends on the number of imperfect maintenance operations conducted since the previous renewal cycle. The sellers offer free perfect and imperfect PM warranty. An optimal periodic maintenance time is determined by minimizing the total cost. Some special cases, implemented with machine learning and human learning, are given to demonstrate the feasibility of the proposed strategy. A numerical example is given.

Optimal preventive policies for imperfect maintenance models with replacement first and last

ABSTRACTThis paper proposes preventive replacement policies for an operating system which may continuously works for N jobs with random working times and is imperfectly maintained upon failure. As a failure occurs, the system suffers one of the two types of failures based on some random mechanism: type-I (repairable or minor) failure is rectified by a minimal repair, or type-II (non repairable or catastrophic) failure is removed by a corrective replacement. A notation of preventive replacement last model is considered in which the system is replaced before any type-II failure at an operating time T or at number N of working times, whichever occurs last. Comparisons between such a preventive replacement last and the conventional replacement first are discussed in detail. For each model, the optimal schedule of preventive replacement that minimizes the mean cost rate is presented theoretically and determined numerically. Because the framework and analysis are general, the proposed models extend several existing results.

A cost minimisation model for joint production and maintenance planning under quality constraints

In this paper, integrated planning of production, imperfect maintenance and process inspections in a multi-machine system is investigated. This system consists of parallel machines which deteriorate with time and they may shift from a primarily in-control state to a degraded state with a higher defective rate or to a failed state. Maintenance scheduling corresponds to a discrete time age-based imperfect maintenance with a large number of maintenance alternatives. Process inspections are considered to detect the current state of the system. Detecting a deteriorated condition initiates the quality check of the related sub-lots, rework of defective items and a process adjustment that brings the machine in its normal conditions. Production planning includes a capacitated lot-sizing problem with multiple products. We propose a joint approach that coordinates the decisions of the three functions, where the objective function minimises the total cost. Evaluation of costs and interacting factors is presented and two heuristic methods are proposed to solve the problem. The results of the joint model are compared to a non-integrated method and a sensitivity analysis is conducted.

Statistical inference for imperfect maintenance models with missing data

The paper considers complex industrial systems with incomplete maintenance history. A corrective maintenance is performed after the occurrence of a failure and its efficiency is assumed to be imperfect. In maintenance analysis, the databases are not necessarily complete. Specifically, the observations are assumed to be window-censored. This situation arises relatively frequently after the purchase of a second-hand unit or in the absence of maintenance record during the burn-in phase. The joint assessment of the wear-out of the system and the maintenance efficiency is investigated under missing data. A review along with extensions of statistical inference procedures from an observation window are proposed in the case of perfect and minimal repair using the renewal and Poisson theories, respectively. Virtual age models are employed to model imperfect repair. In this framework, new estimation procedures are developed. In particular, maximum likelihood estimation methods are derived for the most classical virtual age models. The benefits of the new estimation procedures are highlighted by numerical simulations and an application to a real data set.

A preventive maintenance model for leased equipment subject to internal degradation and external shock damage

A periodic preventive maintenance modeling method is proposed for leased equipment with continuous internal degradation and stochastic external shock damage considered simultaneously, which can facilitate the equipment lessor to optimize the maintenance schedule for the same kind of equipment rented by different lessees. A novel interactive mechanism between the continuous internal degradation and the stochastic external shock damage is established on the hazard rate of the equipment with integrating the imperfect effect of maintenance. Two improvement factors are defined for the modeling of imperfect maintenance. The number of failures resulting from internal degradation and from external shocks are both mathematically deduced based on this interactive mechanism. The optimal preventive maintenance scheme is obtained by minimizing the cumulative maintenance cost throughout the lease period. Numerical example shows that the proposed preventive maintenance model not only can reflect the reliability status of the equipment but also can clearly distinguish between the impact from internal degradation and that from external shocks.

Integrated inventory model with backorder and minimal repair in a supply chain

This study explores the feasibility of applying an integrated inventory model with the consideration of incorporating production programs and maintenance to an imperfect process involving a deteriorating production system. The main target of this research is to build an integrated inventory model with the issues of backorder and repair. Additionally, our proposed model tries to provide an optimal number of shipments m and lower cost. Furthermore, this article offers the detailed discussion of two preventive maintenances of the production run period which are named as perfect preventive maintenance and imperfect preventive maintenance. Also, in this study, we develop various special cases that consider the failure rate such as Weibull, geometric and learning effect. Based on the results, the demand and production ratio influences the holding cost and purchase cost and has the highest impact on the integrated model.

Preventive maintenance for a single-machine system under variable operational conditions

It is a common industrial practice that machines work under variable operational conditions. This article develops an imperfect maintenance model for machines working under variable operational condition, assuming that the failure time distribution of machines under different operational conditions follows the accelerated failure time model. To deal with the case that the future operational conditions’ information over the planning time interval are unknown in advance, it proposes a preventive maintenance policy which is based on current hazard rate function. An example is studied, and the results are compared with the cases that the whole operational condition information is given in advance, and the influence of operational condition is not considered. The results reveal the necessity of integrating operational condition with preventive maintenance optimization. In addition, the performance of the proposed policy has been explored with different parameters of operational conditions.

Performance measures for a deteriorating system subject to imperfect maintenance and delayed repairs

A system subject to an accumulating deterioration and continuous monitoring is analyzed in this article. The system deterioration is modeled using a gamma process, and the system is considered as failed when its degradation level exceeds a failure threshold. The maintenance team lasts a fixed time to start the maintenance actions. To prevent downtime, an alert signal is sent in advance to the maintenance team when the degradation level of the system exceeds a preventive threshold. At the maintenance time, three maintenance actions can be performed: preventive replacement, corrective replacement, and imperfect repair. We assume that the repair is imperfect, in a sense that it reduces a part of the degradation accumulated by the system from the last maintenance action. Under these assumptions, integral equations fulfilled by different performance measures are obtained. Numerical examples are given that illustrate the analytical results.

Optimal imperfect maintenance strategy for leased equipment

This paper presents a preventive maintenance (PM) policy for leased equipment over a finite lease period L. Maintenance is entrusted to the lessor. The purpose is to determine the PM period T minimizing the expected total cost over the lease period in a context where repairs and PM actions are imperfect and have non-negligible durations. The leased equipment is periodically subjected to imperfect PM restoring it to the state ‘as good as new’ with probability p and keeping it at state ‘as bad as old’ with probability q. In case of failures between successive PM actions, imperfect repairs are performed with a given efficiency following a decreasing quasi-renewal process. Besides the maintenance costs, the lessor may incur a penalty cost if the total expected equipment downtime due to repair and PM during the lease period exceeds a pre-specified threshold. A mathematical model and a numerical algorithm are developed to find the optimal maintenance strategy T* over the lease period for any given set of input parameters (equipment reliability, the lease period length, the repair and PM efficiency levels, and the maintenance and penalty costs). A numerical example is presented and the obtained results are discussed.

Optimal production model for imperfect process with imperfect maintenance, minimal repair and rework

ABSTRACTThis study considers the integrated problem of production, preventive maintenance (PM), inspection, and inventory for an imperfect process with minimal repair, PM error and rework. We develop a model for optimising the number of inspections, the inspection interval and the economic production quantity. The model assumes that the production process is in either the in-control state or the out-of-control state. If the production process is in the in-control state, then PM is performed. If the production process is in the out-of-control state, two process shifts are possible. The first type is a relatively mild production process shift in which a minimal repair can restore the process to the in-control state. The second type is a more serious shift that completely stops the production cycle. Numerical analyses are used to investigate the effectiveness of imperfect PM and to explore how profit is affected by minimal repair, PM error and rework.

Optimal Long-Run Imperfect Maintenance With Asymptotic Virtual Age

We consider imperfect, periodic preventive maintenance of items with increasing failure rates, and minimal repairs in-between. The imperfect maintenance is performed in accordance with a Kijima II virtual age model. The obtained deterministic asymptotic value of virtual age defines the starting age of an item at the beginning of each cycle in the formulated and analyzed long-run optimization problem. A similar approach is used for the corresponding age-based preventive maintenance, where the starting age of the renewal-type cycle is already a random variable. Some meaningful examples are considered.

Selective maintenance optimization when quality of imperfect maintenance actions are stochastic

This paper addresses the selective maintenance optimization problem in a multi-component system, carrying out several missions with scheduled inter-mission breaks. To improve the probability of the system successfully completing the next mission, maintenance is performed on the system׳s components during the break. Each component is assigned a list of eligible maintenance actions ranging from minimal repair, through intermediate imperfect maintenance actions, to replacement. The quality of a maintenance action is assumed to be stochastic, reflecting the degree of expertise of the repairman and the tools used to perform the maintenance action. This quality is thus treated as a random variable with an identified probability distribution. The selective maintenance problem aims thus at finding a cost-optimal subset of maintenance actions, to be performed on the system during the limited duration of the break, which guarantees that the pre-set minimum probability of successfully completing the next mission is attained. The fundamental constructs and the relevant parameters of this nonlinear and stochastic optimization problem are developed and thoroughly discussed. It is then put into practice for a series–parallel system and the added value of solving it as a stochastic problem is demonstrated on some test cases.

Reliability optimization of wind farms considering redundancy and opportunistic maintenance strategy

In this paper, a joint redundancy and imperfect block opportunistic maintenance optimization model is formulated. The objective is to determine the wind farm redundancy level and the maintenance strategy which will simultaneously minimize the wind farm loss of load probability and life cycle cost. A new opportunistic maintenance approach is developed to take advantages of the maintenance opportunities. Different reliability thresholds are introduced for imperfect maintenance of failed turbines and working turbines and preventive dispatching of maintenance teams. In addition, a simulation method is developed to evaluate the performance measures of a wind farm system considering different types of wind turbine, maintenance activation delays and durations, and limited number of maintenance teams. Sensitivity analysis is conducted to discuss the influence of the different assumption and parameters of simulation model over the wind farm performance. Pareto optimal solutions are driven based on a multi-objective particle swarm optimization algorithm. Comparative study with the commonly used maintenance policy demonstrates the advantages of the proposed opportunistic maintenance strategy in significantly reducing maintenance cost and loss of load probability.

Selective maintenance optimization for series-parallel systems with continuously monitored stochastic degrading components subject to imperfect maintenance

Abstract: This paper addresses a degradation-based selective maintenance decision problem in a continuously monitored multi-component system, carrying out several missions with scheduled inter-mission breaks. Each system’s component is modeled as a time-dependent stochastic process. Each component is also assigned a list of maintenance actions ranging from imperfect maintenance actions up to complete overhauling. Both corrective and preventive imperfect maintenance are allowed. To improve the probability of the system successfully completing the next mission, maintenance is performed on the system’s components during the break. The selective maintenance problem aims thus at finding a cost-optimal subset of maintenance actions, to be performed on the system during the limited duration of the break, which guarantees the successful completion of the next mission with a minimum reliability level. The fundamental constructs and the relevant parameters of this new nonlinear selective maintenance optimization problem are developed and thoroughly discussed. A numerical example is provided to illustrate the proposed approach.