An adaptive degradation‐based maintenance model taking into account both imperfect adjustments and AGAN replacements

This paper presents an adaptive maintenance model for equipment that can be adjusted (minor preventive maintenance, imperfect state) or replaced (major preventive maintenance, as good as new) at specific scheduled times based on degradation measurements. An initial reliability law that uses a degradation‐based model is built from the collection of hitting times of a failure threshold. Inspections are performed to update the reliability, the remaining useful life, and the optimum time for preventive maintenance. The case of both as good as new replacements and imperfect adjustments is considered. The proposed maintenance model is based on the optimization of the long‐term expected cost per unit of time. The model is then tested on a numerical case study to assess its effectiveness. This results in an improvement for the occurrences of maintenance tasks that minimizes the mean cost per unit of time as well as an optimized number of adjustments that can be considered before replacing an item. The practical application is a decision aid support to answer the 2 following questions: Should we intervene now or wait for the next inspection? For each intervention, should we adjust or replace the item of equipment? The originality is the presence of 2 criteria that help the maintainer to decide to postpone or not the preventive replacement time depending on the measured degradation and to decide whether the item should be adjusted or replaced.     

A literature review on selective maintenance for multi‐unit systems

Selective maintenance is regarded as a type of profit‐generating maintenance policy, playing an important role in balancing limited maintenance resources with system performance. Since 1988, increasing interest has been focused on this research area. Nevertheless, to the best of our knowledge, there is a lack of critical reviews of selective maintenance. This paper is the first systematic review focusing on this relevant topic. In this work, a definition and some specific features of selective maintenance are elaborated. Based on these features, a set of criteria that have been considered in selective maintenance optimization are summarized into 3 categories: system characteristics, maintenance characteristics, and mission profile characteristics. Based on these criteria, a comprehensive literature review on selective maintenance is undertaken. The solution approaches, as well as a general procedure for selective maintenance optimization, are discussed. Finally, some possible directions for further research are provided.     

Optimal preventive maintenance strategy using two repair crews having different efficiencies: a quasi renewal process based modelling approach

This paper considers randomly failing, single-unit equipment subject to a periodic preventive maintenance (PM) policy. In case of failure between successive perfect PM actions (renewals), imperfect repairs are performed following a decreasing quasi-renewal process. One of two different maintenance crews can perform the repairs. One team is more experienced, and consequently more efficient than the other, but more costly. A mathematical model is developed in order to determine the PM period, T, and the kth repair, during a PM period, after which the repair team should be changed, minimising the average total cost per time unit over an infinite time span. It is also proved that an optimal solution in terms of the PM period always exists for any given system lifetime distribution and any set of maintenance costs. Numerical examples are presented and the obtained results are discussed.     

Effects of maintenance policy on an imperfect production system under trade credit

The traditional production model development assumed that all products are perfect quality and did not consider maintenance, which is far from reality. In practice, the production process may shift randomly from an in-control state to an out-of-control state during a production run, i.e. process deterioration. This paper considers both preventive maintenance and corrective maintenance which are used to increase the system reliability. The objective of this paper is to determine the optimal production run time and maintenance frequency while minimising the total cost under process deterioration and trade credit. This paper develops a theorem and an algorithm to solve the problem described, provides numerical analysis to illustrate the proposed solution procedure, and discusses the impact of various system parameters. A real case of hi-tech manufacturer is used to verify the model. It predicts a 10.36% decrease in total cost if the preventive maintenance decision is considered.     

Predictive maintenance policy based on process data

For the ‘under maintained’ and ‘over maintained’ problems of traditional preventive maintenance, a new predictive maintenance policy is developed based on process data in this article to overcome these disadvantages. This predictive maintenance method utilizes results of probabilistic fault prediction, which reveals evolvement of the system's degradation for a gradually deteriorating system caused by incipient fault. Reliability is calculated through the fault probability deduced from the probabilistic fault prediction method, but not through prior failure rate function which is difficult to be obtained. Moreover, the deterioration mode of the system is determined by the change rate of the calculated reliability, and several predictive maintenance rules are introduced. The superiority of the proposed method is illustrated by applying it to the Tennessee Eastman process. Compared with traditional preventive maintenance strategies, the presented predictive maintenance strategy shows its adaptability and effectiveness to the gradually deteriorating system.     

A cost‐based selective maintenance decision‐making method for machining line

Machine line is a type of manufacturing system in which machines are connected in series or in parallel. It is significant to ensure the reliability as well as to reduce the total cost of maintenance and failure losses in the maintenance programs of such systems. Cost‐based selective maintenance decision‐making, which is the best method for a selected group of machines in machine line is presented under limited maintenance durations. Fault losses and maintenance costs of a single machine under different maintenance actions i.e. minimal repair, preventive maintenance and overhaul on the fault rate of the machine are calculated. An algorithm combining the heuristic rules and tabu search is proposed to solve the presented selective maintenance model. Finally, a case study on the maintenance decision‐making problem of a connecting rod machining line in the automobile engine workshop is presented to illustrate the applicability of the proposed method. The end result shows that the fault losses can be further reduced by the optimization of maintenance interval and maintenance duration. Copyright © 2010 John Wiley & Sons, Ltd.     

Reliability modeling of multi‐state degraded repairable systems and its applications to automotive systems

In this paper, we presented a continuous‐time Markov process‐based model for evaluating time‐dependent reliability indices of multi‐state degraded systems, particularly for some automotive subsystems and components subject to minimal repairs and negative repair effects. The minimal repair policy, which restores the system back to an “as bad as old” functioning state just before failure, is widely used for automotive systems repair because of its low cost of maintenance. The current study distinguishes with others that the negative repair effects, such as unpredictable human error during repair work and negative effects caused by propagated failures, are considered in the model. The negative repair effects may transfer the system to a degraded operational state that is worse than before due to an imperfect repair. Additionally, a special condition that a system under repair may be directly transferred to a complete failure state is also considered. Using the continuous‐time Markov process approach, we obtained the general solutions to the time‐dependent probabilities of each system state. Moreover, we also provided the expressions for several reliability measures include availability, unavailability, reliability, mean life time, and mean time to first failure. An illustrative numerical example of reliability assessment of an electric car battery system is provided. Finally, we use the proposed multi‐state system model to model a vehicle sub‐frame fatigue degradation process. The proposed model can be applied for many practical systems, especially for the systems that are designed with finite service life.     

Fatigue assessment and failure analysis of shot‐peened leaf springs

The paper deals with the fatigue and failure analysis of serial shot‐peened leaf springs of heavy trucks emphasizing on the influence of thermal treatment and shot peening on fatigue life. Experimental stress–life curves are determined by investigating smooth specimens subjected to fully reversed rotating bending conditions. These test results are compared to corresponding ones determined from cyclic three‐point bend tests on shot‐peened serial leaf springs in order to reveal the influence of the applied thermal treatment and shot peening process on the fatigue life of the high‐strength steel used for leaf spring manufacturing, dependent on the load level. Microstructure, macro‐ and micro‐hardness analyses are performed to support the analyses and explain the effects resulting from the certain shot peening process on the surface properties of the high‐strength spring steel under investigation. The assessment of the fatigue results reveals nearly no life improvement due to the manufacturing, emphasizing the necessity for mutual adjustment of shot peening and thermal treatment parameters to take account for life improvement.     

Reliability evaluation and opportunistic maintenance policy based on a novel delay time model

The delay time concept is widely adopted in literature to model the two‐stage failure process of most industrial systems which can be divided into normal stage (from new to an initial point of a defect) and defective stage (from defect arrival point to failure). Most existing delay time models assume that the normal and defective stages are independent. A generalized delay time model is proposed in this paper by considering the dependence between the normal and defective stages which is reflected in the fact that they share the same external shock process. According to the definition of shot‐noise process, external shocks will incur random hazard rate increments in the two stages. The failure state is self‐announcing, whereas the defective state can only be detected by block‐based inspection or opportunistic inspection offered by unexpected shutdown due to unavoidable external factors. The system is correctively replaced upon the occurrence of a system failure or preventively replaced at the detection of a defective state. Based on the stochastic failure model and maintenance policy, this paper evaluates system reliability performance and average long‐run cost rate via a Markov‐chain based approach. Finally, a case study on a steel convertor plant is given to demonstrate the applicability of the proposed model.     

A data-driven maintenance policy for railway wheelset based on survival analysis and Markov decision process

Wheelsets absorb a significant part of the maintenance budget of any train operating company. Although wheel wear has been an extensively discussed topic in the literature, wear rates are very rarely characterized by using degradation data in a real-world case study aimed at identifying optimal maintenance policies including both degradation and recovery modeling. Furthermore, wheel defects, which impose an additional challenge to the modeling of the lifecycle of the wheels, are usually considered separately in the literature. In this study, conducted at a Portuguese train operating company, 17 years of inspection data are used to estimate wheel wear rates and survival curves, which are further incorporated into a Markov decision process (MDP) model. A bidimensional framework considering discrete intervals of wheel diameter along with a quantitative variable (kilometers since last turning/renewal) is used to represent the possible wheel states, while the probability of a defect interfering with the wheel maintenance schedule is modeled by contemplating survival curves derived from a Cox proportional-hazards model. Optimal results in terms of minimal cost policy are discussed in the context of the MDP, but a more realistic and easy-to-implement policy fixing one of the parameters is compared with the optimal policy. Results showed that in practice train operating companies might benefit from using the easy-to-implement policy, which has an associated long-run average cost only about 1% higher than the one suggested by the optimal decision map.     

Pattern‐based prognostic methodology for condition‐based maintenance using selected and weighted survival curves

This paper proposes a pattern‐based prognostic methodology that combines logical analysis of data (LAD) as an event‐driven diagnostic technique, and Kaplan–Meier (KM) estimator as a time‐driven technique. LAD captures the effect of the instantaneous conditions on the health state of a monitored system, while KM estimates the baseline reliability curve that reflects the effect of aging, based on the observed historical failure times. LAD is used to generate a set of patterns from the observed values of covariates that represent the operating conditions and condition indicators. A pattern selection procedure is carried out to select the set of significant patterns from all the generated patterns. A survival curve is estimated, for each subset of observations covered by each selected pattern. A weight that reflects the coverage of each pattern is assigned to its survival curve. Given a recently collected observation, the survival curve of a monitored system is updated on the basis of the patterns covering that observation. The updated curve is then used to predict the remaining useful life of the monitored system. The proposed methodology is validated using a common dataset in prognostics: the turbofan degradation dataset that is available at NASA prognostic repository. Copyright © 2017 John Wiley & Sons, Ltd.     

Reliability‐based Optimal Maintenance Scheduling by Considering Maintenance Effect to Reduce Cost

A new reliability‐based optimal maintenance scheduling method is presented that considers the effect of maintenance in reducing costs. An ordering list of element maintenance effects with various maintenance‐interval types is constructed. By means of this ordering list, reliability‐based optimal maintenance scheduling for simple reliability structures and composite reliability systems is then carried out. The properties of the proposed method, such as the evaluation of maintenance cost reduction, the simplicity of the proposed method by sacrificing system availability within the allowance method, the operation decision based on the optimal maintenance schedule, etc., are discussed. With simulations, the effectiveness of the proposed method is verified. Copyright © 2005 John Wiley & Sons, Ltd.     

A study of time‐between‐events control chart for the monitoring of regularly maintained systems

Owing to usage, environment and aging, the condition of a system deteriorates over time. Regular maintenance is often conducted to restore its condition and to prevent failures from occurring. In this kind of a situation, the process is considered to be stable, thus statistical process control charts can be used to monitor the process. The monitoring can help in making a decision on whether further maintenance is worthwhile or whether the system has deteriorated to a state where regular maintenance is no longer effective. When modeling a deteriorating system, lifetime distributions with increasing failure rate are more appropriate. However, for a regularly maintained system, the failure time distribution can be approximated by the exponential distribution with an average failure rate that depends on the maintenance interval. In this paper, we adopt a modification for a time‐between‐events control chart, i.e. the exponential chart for monitoring the failure process of a maintained Weibull distributed system. We study the effect of changes on the scale parameter of the Weibull distribution while the shape parameter remains at the same level on the sensitivity of the exponential chart. This paper illustrates an approach of integrating maintenance decision with statistical process monitoring methods. Copyright © 2008 John Wiley & Sons, Ltd.     

Setting reliability requirements based on minimum failure‐free operating periods

A new reliability methodology and tools have been created for setting reliability requirements. At the heart of the new methodology are reliability requirements based on specified minimum failure‐free operating (MFFOP) intervals and a maximum acceptable level of the probability of premature failure. These types of requirements are suitable to industries where the consequences of failure and the cost of intervention for maintenance are very high (e.g. deepwater offshore oil and gas industries). The methodology proposed includes models and tools for: (i) setting reliability requirements to limit the risk of premature failure below an acceptable level; (ii) setting reliability requirements to minimize the total losses; and (iii) setting reliability requirements to guarantee a set of MFFOP intervals. An advantage of the MFFOP approach is that it directly links the reliability requirements with health, safety, environmental and business risks. Another advantage is that the MFFOP requirements are suitable for non‐constant hazard rates where the mean time to failure (MTTF) reliability measure is often misleading. A solution to the important problem of determining the maximum hazard rate that guarantees with a required probability the existence of a specified set of MFFOP intervals has also been found. The reliability tools proposed also permit the extraction of useful information from data sets containing a given number of random failures, in cases where the failure times are unknown. Copyright © 2003 John Wiley & Sons, Ltd.     

Optimal design for resilient load‐sharing systems with nonidentical components

For system maintenance, strategic component restoration planning is an important conceptual framework for load‐sharing k‐out‐of‐n: G system. A cost‐effective treatment of failure events is imperative with the purpose of reinstating the system ability. This paper presents a new optimal design method for load‐sharing repairable k‐out‐of‐n: G system, in which a flowgraph is used in conjunction with multiresponse optimization. By introducing the concept of modular design, the system is partitioned into scalable and repairable maintenance modules. The determination of the optimal design depends on the type of system components, the module‐based system structure, and the repair rule setting. An extended flowgraph model, which links covariates into transition branches, is used for modeling the system failure evolution. With consideration of various system performance measurements as responses, multiresponse optimization with weighted principal component analysis is used to achieve an optimal design of maintenance modules as well as repair policy. The methodology presented in this paper provides an efficient way to design a system having nonidentical components and arbitrary repair time distributions with consideration of the variety of maintenance policies as well as the diversity of system operating conditions.     

A note on MTTF formulae for redundant systems with Erlangian‐distributed unit and common‐cause times to failure

This communication presents newly developed mean‐time‐to‐failure (MTTF) formulae for systems with Erlangian‐distributed unit and common‐cause times to failure. These systems are parallel, triple modular redundant (TMR), r‐out‐of‐m, bridge, parallel‐series and series‐parallel. Copyright © 2000 John Wiley & Sons, Ltd.     

Real‐time Reliability Evaluation with a General Wiener Process‐based Degradation Model

The aim of this paper is to investigate the issue of real‐time reliability evaluation based on a general Wiener process‐based degradation model. With its mathematical tractability, the Wiener process with a linear drift has been widely used in the literature, to characterize the dynamics of the degradation process or its transformation. However, the nonlinear degradation process, which can't be properly linearized, exists in practice. The dynamics of such a degradation process can't be accurately captured by linear models. Here, a general Wiener process‐based degradation model is proposed, which covers a variety of Wiener process‐based models as its limiting cases. A two‐stage method is presented to estimate the unknown parameters. Two real‐time reliability evaluation procedures are presented for different conditions: one is the analytical evaluation procedure, and the other is the simulated evaluation procedure. It is shown that when new degradation information is available, the evaluation results can be adaptively updated. Moreover, to check out the proposed degradation model, a graphical method is provided. Finally, the validity of the proposed evaluation method is illustrated by a numerical example and two real‐world examples. Copyright © 2013 John Wiley & Sons, Ltd.     

液力减振器结构异响发生的微过程分析

将液力减振器的工作循环划分成更细微的过程并相应地进行了力学分析,建立了相应的微分方程,并利用M ATLAB软件进行了仿真计算,分析计算结果与台架试验结果一致。结果表明,活塞与油液间的间隙碰撞、活塞与缸筒的静动摩擦力交变和阀片与活塞的粘附均导致对汽车液力减振器活塞的冲击,其进一步的传递将产生结构异响。     

The construction of robust mixture‐process experimental designs via genetic algorithm

Mixture experiments with the presence of process variables are commonly encountered in the manufacturing industry. The experimenter who plans to conduct mixture experiments in which a process involves the combination of machines, methods, and other resources will try to find condition of design factors which make the product/process insensitive or robust to the variability transmitted into the response variable. We propose the genetic algorithm (GA) for generating robust mixture‐process experimental designs involving control and noise variables. When the noise variables, which are extremely difficult to control or not routinely controlled during the manufacturing process and may change without warning, are considered in a mixture experiment, we propose the robust design setting. When considering a robust design, the design that has a lower and flatter faction of design space curves for all levels of the controllable process variables at varying noise interaction is preferable. We evaluate the designs with respect to these criteria for both the mean model and the slope model. The evaluation demonstrates that the proposed GA designs are robust to the contribution of the interactions involving the noise variables.     

A study of the modeling and analysis of software fault‐detection and fault‐correction processes

Most of the models for software reliability analysis are based on reliability growth models which deal with the fault detection process. This is done either by assuming that faults are corrected immediately after being detected or the time to correct a fault is not counted. Some models have been developed to relax this assumption. However, unlike the fault‐detection process, few published data sets are available to support the modeling and analysis of both the fault detection and removal processes. In this paper, some useful approaches to the modeling of both software fault‐detection and fault‐correction processes are discussed. Further analysis on the software release time decision that incorporates both a fault‐detection model and fault‐correction model is also presented. This procedure is easy to use and useful for practical applications. The approach is illustrated with an actual set of data from a software development project. Copyright © 2006 John Wiley & Sons, Ltd.