Preventive maintenance scheduling for repairable system with deterioration

Maintenance as an important part in manufacturing system can keep equipment in good condition. Many maintenance policies help to decrease the unexpected failures and reduce high operational cost such as conventional preventive maintenance. But these conventional preventive maintenance policies have the same time interval T that may easily neglect system’s reliability, because the system deteriorates with increased usage and age. Hence, this study has developed a reliability-centred sequential preventive maintenance model for monitored repairable deteriorating system. It is supposed that system’s reliability could be monitored continuously and perfectly, whenever it reaches the threshold R, the imperfect repair must be performed to restore the system. In this model, system’s failure rate function and operational cost are both considered by the effect of system’s corresponding condition, which helps to decide the optimal reliability threshold R and preventive maintenance cycle number. Finally, through case study, the simulation results show that the improved sequential preventive maintenance policy is more practical and efficient.     

Periodic and sequential preventive maintenance policies over a finite planning horizon with a dynamic failure law

The purpose of this study is to propose and model periodic and sequential preventive maintenance policies for a system that performs various missions over a finite planning horizon. Each mission can have different characteristics that depend on operational and environmental conditions. These proposed preventive maintenance policies are defined and modeled mathematically. The study of these two policies is based on a dynamic system failure law that takes into account the different missions performed. The first step is to determine the optimal business plan to achieve, i.e. the set of missions to perform in order to maximize the profit of missions minus maintenance costs. Thus, for each plan, we determine the maintenance planning considering two policies. The first preventive maintenance policy is periodic and the objective is to determine the optimal number of preventive maintenance to achieve. For the second policy, namely sequential, we calculate the optimal number of preventive maintenance intervals and the duration of these different intervals.     

Generalized sequential preventive maintenance policy of a system subject to shocks

The paper proposes and analyses a generalized sequential preventive maintenance policy of a system subject to shocks. The shocks arrive according to a non-homogeneous Poisson process {N i (t); t S 0 }, whose intensity function r i (t) varies with the number of maintenance actions (i - 1 ) that have already been carried out, and the time (t) that has elapsed since the last maintenance action. Upon the arrival of the k th shock, the system is maintained or repaired minimally with probability θ i , k and q i , k respectively depending on the number of maintenance actions (i - 1 ) that have already occurred and the ordinal number of the arriving shock (the k th ) since the last maintenance. In addition, a planned maintenance is carried out as soon as T i time units have elapsed since the (i - 1 ) th maintenance action. If i = N, the system is replaced rather than maintained. The objective is to determine the optimal plan (in terms of N and T i ) that minimizes the expected cost per unit of time. It is shown that under certain reasonable assumptions, a sequential preventive maintenance policy has unique solutions. Various special cases are considered.     

Two-stage Maintenance of a Production System with Exponentially Distributed On- and Off-periods

We consider an on–off production system which is subject to failure during on-periods. In case of a failure, the production system is maintained correctively. In addition, preventive maintenance is carried out to prevent failures. The costs of both preventive and corrective maintenance are modelled in terms of the net down-time of the production system, that is, the time that the system is not available when needed for production. Since preventive maintenance can be planned during off-periods, there is a perspective of significant savings if some freedom is built in concerning the starting time of preventive maintenance. Therefore, a two-stage maintenance policy is considered, which—in a first stage—provides the maintenance manager with a finite interval during which preventive maintenance must be carried out, and—in a second stage—determines the optimal starting time for preventive maintenance within this interval. Computational results offer useful insights, and indicate that significant savings can be achieved in comparison with a classical age maintenance policy.     

Optimum preventive maintenance policies for systems subject to random working times,replacement, and minimal repair

This paper proposes, from the economical viewpoint of preventive maintenance in reliability theory, several preventive maintenance policies for an operating system that works for jobs at random times and is imperfectly maintained upon failure. As a failure occurs, the system suffers one of two types of failure based on a specific random mechanism: type-I (repairable) failure is rectified by a minimal repair, and type-II (non-repairable) failure is removed by a corrective replacement. First, a modified random and age replacement policy is considered in which the system is replaced at a planned time T, at a random working time, or at the first type-II failure, whichever occurs first. Next, as one extended model, the system may work continuously for N jobs with random working times. Finally, as another extended model, we might consider replacing an operating system at the first working time completion over a planned time T. For each policy, the optimal schedule of preventive replacement that minimizes the mean cost rate is presented analytically and discussed numerically. Because the framework and analysis are general, the proposed models extend several existing results.     

An integrated strategy for efficient business plan and maintenance plan for systems with a dynamic failure distribution

The purpose of this study is to propose an integrated strategy to determine jointly efficient business and maintenance plans. The studied system is subject to random failures with a dynamic failure law. It must perform a set of missions (among M possible missions) over a finite planning horizon. Each mission may have different characteristics that depend on operational and environmental conditions. The determination of a business plan consists in choosing and scheduling the missions to be performed. To maximize the net profit (profits generated by the achievement of missions minus maintenance costs), two meta-heuristics based on genetic algorithms are developed. The first genetic algorithm is used to determine the business plan and the second one generates an efficient maintenance plan. Two maintenance policies are studied: a minimalist policy which involves only corrective maintenance actions and another policy, called sequential, which involves several imperfect preventive maintenance activities performed at predetermined times. Two cases are studied for the latter strategy. The first one considers the maintenance effectiveness factor as being the same for all preventive maintenance actions and we search for the best factor. In the second case, we consider maintenance actions with different efficiency factors and we look for the optimal value of each factor. Finally, a numerical example illustrates the proposed approach and the difference between the maintenance policies.     

Risk based opportunistic maintenance model for complex mechanical systems

The essence of the complex mechanical system can be considered as an open system. There exist coupling relationships between various parts in the system and also between different fault modes, which result in multiple fault propagation paths. Considering the safety, benefits and maintenance loss, parameters such as downtime losses, minimal repair costs, corrective, preventive and opportunistic maintenance costs, should be analyzed comprehensively to investigate the influence of different maintenance strategies.A new risk based opportunistic maintenance (RBOM) model considering failure risk is proposed in the paper. It helps to convert the negative random factors caused by single faults to a favorable opportunity of preventive defense against failure for other slight degraded components in advance, so the overall economic losses could be reduced. The global optimization algorithm is further developed to realize RBOM policy. Case studies are provided to illustrate the proposed approaches, with sensitivity analysis of the position, time, style and criterion of the RBOM strategy. Comparative study with the widely used maintenance policy demonstrates the advantage of the proposed method can significantly reduce the maintenance cost and failure risk, and are expected to bring immediate benefits to the energy industry.     

Optimal maintenance policy incorporating system level and unit level for mechanical systems

The study works on a multi-level maintenance policy combining system level and unit level under soft and hard failure modes. The system experiences system-level preventive maintenance (SLPM) when the conditional reliability of entire system exceeds SLPM threshold, and also undergoes a two-level maintenance for each single unit, which is initiated when a single unit exceeds its preventive maintenance (PM) threshold, and the other is performed simultaneously the moment when any unit is going for maintenance. The units experience both periodic inspections and aperiodic inspections provided by failures of hard-type units. To model the practical situations, two types of economic dependence have been taken into account, which are set-up cost dependence and maintenance expertise dependence due to the same technology and tool/equipment can be utilised. The optimisation problem is formulated and solved in a semi-Markov decision process framework. The objective is to find the optimal system-level threshold and unit-level thresholds by minimising the long-run expected average cost per unit time. A formula for the mean residual life is derived for the proposed multi-level maintenance policy. The method is illustrated by a real case study of feed subsystem from a boring machine, and a comparison with other policies demonstrates the effectiveness of our approach.     

存在故障相关及不完备检测的主辅并联系统可靠性建模与维修策略

主辅部件并联普遍存在于机电系统中, 部件间存在故障相关性等因素使得针对系统的可靠性建模和维修策略优化尤为困难. 本文以部件间存在I类和III类故障相关性及因检测系统的不完备引起主部件累积损伤速率分布改变的主辅并联系统为对象, 通过对系统退化过程的分析建立其可靠性模型. 依据所建模型和观测的辅助部件的故障次数及系统的运行时间, 建立(T, N)预防性更换策略优化模型. 通过案例分析检测系统的完备性、累积损伤速率等参数对系统可靠性及预防性维修策略的影响.     

Computational aspects of an extended EMQ model with variable production rate

The paper considers a generalized economic manufacturing quantity (EMQ) model with stochastic machine breakdown and repair in which the time to machine failure, corrective and preventive repair times are all assumed to be random variables. The model is formulated under general failure and general repair time distributions, treating the machine production rate (speed) as a decision variable. As the stress condition of the machine changes with the production rate, the failure rate is assumed to be dependent on the production rate. The model is further extended to the case where certain safety stocks are hold in inventory to protect against possible stockout during machine repair. The solution procedure and computational algorithms of the associated constrained optimization problems are provided. Numerical examples are taken to determine the optimal production policies by the proposed algorithms and examine the sensitivity of the model parameters.Several economic manufacturing quantity (EMQ) models for unreliable manufacturing systems have been developed in the literature even for general failure and general repair (corrective) time distributions. In these studies, preventive repair has not been considered in a general way and efforts have been made to derive the production control and maintenance policy for inflexible manufacturing systems, where the machine capacity is pre-determined. The purpose of this article is to formulate a generalized EMQ model for a flexible unreliable manufacturing system in which (i) the time to machine failure and repair (corrective and preventive) times follow general probability distributions and (ii) the machine failure rate is dependent on the production rate. Consideration of a variable production rate makes the model hard to analyze completely. So, attempt has also been made to get into its computational aspects by developing solution algorithms.     

EXPERT-MM: A knowledge-based system for maintenance management

This paper presents an approach to the design and development of knowledge-based systems in general and their application in the field of maintenance management in particular. Our approach is based on the idea that different kinds of knowledge in a given domain, namely declarative, procedural and heuristic are supported by corresponding methods and software tools. A prototype knowledge-based system, called EXPERT-MM, for the maintenance activities in the Siam Gipsum Industry (Bangkok, Thailand) has been worked out as a case study and is described in the paper. EXPERT-MM supports three main functions: maintenance policy suggestions, machine diagnosis and maintenance scheduling. The maintenance policy deals with the three types of preventive maintenance. For each component of the equipment it analyses the historical failure data and recommends an appropriate policy with optimal preventive maintenance intervals. This is based on the experts' knowledge stored in a knowledge base. A rotary screw type air compressor is selected for a diagnosis. The knowledge representation scheme is rule-based and the inference strategy mechanism is backward chaining. The knowledge-acquisition process has been organized and realised using a decision tree diagram. The knowledge base contains 154 rules for the diagnosis and 54 rules for the maintenance model selection. the maintenance scheduling module is procedure based. EXPERT-MM development is based on the software tools dBase III Plus, TURBO PASCAL version 6.0 and expert system shell EXSYS, all integrated into a single software system with a user-friendly interface.     

Degradation prediction and rolling predictive maintenance policy for multi-sensor systems based on two-dimensional self-attention

Traditional preventive maintenance policy gradually failed to guarantee the security and economy of current mechanical systems. This paper proposed a highly efficient rolling predictive maintenance (RPdM) policy for multi-sensor system, to make maintenance decisions. In this policy, to cope with the uncertainty of remaining useful life (RUL) prediction, the degradation process of the system is first divided into four intervals according to the inspection interval and spare parts lead time. Then, the two-dimensional self-attention (TDSA) method, which extract time dimensional and feature dimensional features by parallel computation, is developed to predict the probabilities of system RUL in the four intervals instead of the point of RUL. In addition, the output probabilities of the TDSA method are utilized to calculate the maintenance cost rate of the current inspection point and future point. The maintenance decision including spare parts ordering time and maintenance time is determined by comparing the maintenance cost rate of each inspection point, and the decision is updated at the next inspection point. To verify the effectiveness of the proposed RPdM policy, the C-MAPSS dataset provided by NASA is employed to implement degradation prediction and maintenance decision. Experiment results show that the cost rate of RPdM policy is lower than preventive maintenance policy, and only 27.7% higher than ideal maintenance policy which is impossible in real engineering. Moreover, the impact of different out-of-stock costs and corrective costs are explored and shows the good robustness of the RPdM policy.     

Energy-oriented joint optimization of machine maintenance and tool replacement in sustainable manufacturing

With the increasing attention on sustainable manufacturing, operation and maintenance (O&M) management focuses on not only budget limit, but also energy saving. For modern CNC systems, besides the energy consumption to operate and maintain the machine, a majority of energy consumption generated from tool wear should be considered. It means both machine degradation and tool wear are required to be modelled for the global saving energy. Thus, this paper proposes an energy-oriented joint optimization of machine maintenance and tool replacement (EJMR) policy by integrating energy consumption mechanisms and joint maintenance opportunities in a machine-tool system. The key issue is to combine the preventive maintenance (PM) scheduling of the machine and the polish/preventive replacement (PR) optimization of sequential tools to form energy-effective schemes. Therefore, joint maintenance opportunities of PM actions are utilized to perform tool polish/PR based on energy consumption mechanisms. Four successive procedures (energy consumption analysis, energy-oriented PM scheduling, machine-tool PR model and integrated decision-making process) are developed. Thereby optimal intervals of machine PM and tool polish/PR are obtained to save energy. The case study illustrates that compared with conventional maintenance policies, this proposed EJMR policy can significantly reduce the total non-value-added energy consumption (TNVE) in sustainable manufacturing.     

Analysis of maintenance policies for finite life-cycle multi-state systems

Maintenance policies for multi-state systems (MSS) are often analyzed under infinite horizon assumptions. In practice, it is important to consider maintenance policies under a finite horizon because the life cycles of most systems are finite. In this paper, we consider a finite life-cycle MSS that is subject to both degradation and Poisson failures. We study two classes of maintenance policies – preventive replacements and corrective replacements, and their effectiveness in controlling the customer’s expected discounted maintenance cost (EDMC). For both policies, replacement decisions are modelled via two control parameters – a threshold on the current system state and a threshold on the residual life cycle, which is measured as the time span from present to the end of life cycle. We derive close-to-explicit forms of the cost models under each of the policy. Methodologies for optimizing the maintenance thresholds are further proposed. Computational results verify that preventive replacements outperform corrective replacements typically when the downtime cost per failure is relatively high compared to the repair cost.     

基于优化策略的串并联系统预防维修周期研究

为了进行预防维修,提高系统可靠性,对系统维修费用会随着维修次数的增加而上升问题,提出最小优化方法.针对工程中常用的串并联系统,在考虑故障率随维修次数变化的条件下,引入役龄回退因子,提出一种可靠度为约束的系统预防维修费用最小的优化模型,在运用遗传算法进行优化求解的基础上,研究了系统总维修费用随可靠度的变化率.为了更好地确保遗传算法获得全局最优解,在传统遗传算法的基础上采用了并行操作、保留最优个体等方法.通过实例验证了优化模型的正确性与可用性,对模型作进一步的扩展,还可用于更一般复杂的系统.     

Analysis of maintenance policies for finite life-cycle multi-state systems

Maintenance policies for multi-state systems (MSS) are often analyzed under infinite horizon assumptions. In practice, it is important to consider maintenance policies under a finite horizon because the life cycles of most systems are finite. In this paper, we consider a finite life-cycle MSS that is subject to both degradation and Poisson failures. We study two classes of maintenance policies – preventive replacements and corrective replacements, and their effectiveness in controlling the customer’s expected discounted maintenance cost (EDMC). For both policies, replacement decisions are modelled via two control parameters – a threshold on the current system state and a threshold on the residual life cycle, which is measured as the time span from present to the end of life cycle. We derive close-to-explicit forms of the cost models under each of the policy. Methodologies for optimizing the maintenance thresholds are further proposed. Computational results verify that preventive replacements outperform corrective replacements typically when the downtime cost per failure is relatively high compared to the repair cost.     

A simulation study of sequencing and maintenance decisions in a dynamic job shop

This paper presents a simulation study of the performance of a job shop, where the equipment is subject to failure. We evaluate the effectiveness of some maintenance scheduling techniques under conditions involving shop load, job sequencing rule, preventive maintenance policy and maintenance capacity.     

考虑维修优先权的多状态温贮备系统可靠性模型

针对工程应用中装备维修和保养两类活动具备不同优先级这一问题,考虑单一维修台可进行维修和保养两类工作,其中维修具有优先权.以多状态温贮备系统为研究对象,采用可近似拟合任意分布的Phase-type(PH)分布构建一种描述能力更强的系统可靠性模型,得出系统稳态可用度、系统故障率、平均故障间隔时间等一系列可靠性指标的解析表达式.最后利用算例验证PH分布的适用性,演示系统可靠性函数随时间的变化趋势,并讨论维修台工作速率对系统故障率、稳态可用度、维修台忙期稳态概率和平均故障间隔时间的影响.算例结果表明,所提模型具有较广泛的应用价值.     

A preventive maintenance policy for a standby system subject to internal failures and external shocks with loss of units

In many situations, serious damage and considerable financial losses are caused by non-repairable failures of a system. Redundant systems and maintenance policies are commonly employed to improve reliability. This paper is focused on the modelling of a complex cold standby system by analysing the effectiveness and costs of preventive maintenance, always in an algorithmic form. The online unit of the system is subject to wear failures and external shocks. The online unit can go through an indeterminate number of degradation levels before failure. This one is observed when inspections occur. Inspections are performed at random intervals, and when one takes place, the unit is taken to the preventive maintenance facility if it is necessary. The preventive maintenance time and cost is different depending on the degradation level observed. If only one unit is performing, a minimal maintenance policy is adopted in order to optimise system behaviour. Reliability measures such as the conditional probability of failure are worked out in a well-structured and algebraic form in transient and stationary regimes by using algorithmic methods. The stationary distribution is calculated using matrix analytic methods, and rewards are included in the model. An optimisation example shows the versatility of the model presented.     

Determination of aggregate preventive maintenance programs using production schedules

Scheduling preventive maintenance in a multi-period and multi-product situation has been dealt with in this paper. An aggergate preventive maintenance programme is determined based on the time of failure distribution, machine load in every period and varying cost of breakdown in different periods; using a steppingstone algorithm. Sample results from the computer programme written for solving this problem are given. This algorithm can be used to solve similar problems.