Optimal maintenance of multi-component systems: a review

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

Applications of Repeated Games in Wireless Networks: A Survey

A repeated game is an effective tool to model interactions and conflicts for players aiming to achieve their objectives in a long-term basis. Contrary to static noncooperative games that model an interaction among players in only one period, in repeated games, interactions of players repeat for multiple periods; and thus the players become aware of other players' past behaviors and their future benefits, and will adapt their behavior accordingly. In wireless networks, conflicts among wireless nodes can lead to selfish behaviors, resulting in poor network performances and detrimental individual payoffs. In this paper, we survey the applications of repeated games in different wireless networks. The main goal is to demonstrate the use of repeated games to encourage wireless nodes to cooperate, thereby improving network performances and avoiding network disruption due to selfish behaviors. Furthermore, various problems in wireless networks and variations of repeated game models together with the corresponding solutions are discussed in this survey. Finally, we outline some open issues and future research directions.Comment: 32 pages, 15 figures, 5 tables, 168 reference

Supporting group maintenance through prognostics-enhanced dynamic dependability prediction

Condition-based maintenance strategies adapt maintenance planning through the integration of online condition monitoring of assets. The accuracy and cost-effectiveness of these strategies can be improved by integrating prognostics predictions and grouping maintenance actions respectively. In complex industrial systems, however, effective condition-based maintenance is intricate. Such systems are comprised of repairable assets which can fail in different ways, with various effects, and typically governed by dynamics which include time-dependent and conditional events. In this context, system reliability prediction is complex and effective maintenance planning is virtually impossible prior to system deployment and hard even in the case of condition-based maintenance. Addressing these issues, this paper presents an online system maintenance method that takes into account the system dynamics. The method employs an online predictive diagnosis algorithm to distinguish between critical and non-critical assets. A prognostics-updated method for predicting the system health is then employed to yield well-informed, more accurate, condition-based suggestions for the maintenance of critical assets and for the group-based reactive repair of non-critical assets. The cost-effectiveness of the approach is discussed in a case study from the power industry

After-sales services optimisation through dynamic opportunistic maintenance: a wind energy case study

After-sales maintenance services can be a very profitable source of incomes for original equipment manufacturers (OEM) due to the increasing interest of assets’ users on performance-based contracts. However, when it concerns the product value-adding process, OEM have traditionally been more focused on improving their production processes, rather than on complementing their products by offering after-sales services; consequently leading to difficulties in offering them efficiently. Furthermore, both due to the high uncertainty of the assets’ behaviour and the inherent challenges of managing the maintenance process (e.g. maintenance strategy to be followed or resources to be deployed), it is complex to make business out of the provision of after-sales services. With the aim of helping the business and maintenance decision makers at this point, this paper proposes a framework for optimising the incomes of after-sales maintenance services through: 1) implementing advanced multi-objective opportunistic maintenance strategies that sistematically consider the assets’ operational context in order to perform preventive maintenance during most favourable conditions, 2) considering the specific OEMs’ and users’ needs, and 3) assessing both internal and external uncertainties that might condition the after-sales services’ success. The developed case study for the wind energy sector demonstrates the suitability of the presented framework for optimising the after-sales services.EU Framework Programme Horizon 2020, MSCA-RISE-2014: Marie Skłodowska-Curie Research and Innovation Staff Exchange (RISE) (grant agreement number 645733- Sustain-Owner-H2020-MSCA-RISE-2014) and the EmaitekPlus 2016-2017 Program of the Basque Government

Extension of the predictive policy to a series of mechanical systems

In the literature, a great interest is reserved to complex systems (i.e. serial or parallel or mixed systems), constituted by the interconnection of single elements. The evolution of system reliability depends on its structure as well as on the evolution of the reliability of its individual elements. Maintenance activities on systems strongly affect element aging and system\u2019s operating life. Preventive maintenance, for example, is used to increase system availability reducing, as a consequence, the probability of failure. Generally, maintenance plans are performed with respect to some criteria depending on cost or on reliability/availability requirements. Therefore, the optimum maintenance scheduling of a system can be based on the minimization of the total cost or on the maximization of its availability. Many Authors emphasize the requirement on system reliability. In [1], for example, the concept of reliability equivalence from simple series and parallel systems to some complex systems is presented and reliability equivalence factors of complex systems are obtained. One of the most critical problems in preventive maintenance is the determination of the optimum frequency to perform maintenance actions on systems, in order to ensure a pre-defined level of availability. In this paper the predictive maintenance policy, for a single element, is extended to a system constituted by two series elements, named A and B. The transition from a single unit to a series system is not immediate and presents a great number of problems. Actually, when a maintenance action is scheduled for a system of this kind, the decision maker must decide if it is more convenient (with respect to some chosen criterion) to intervene on element A or B or on both. The proposed methodology deals with this practical problem in the context of the predictive maintenance policy. Research on this topic is in a running state and the methodology is only theoretically presented

Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs

Optimal maintenance of multi-component systems: a review

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

Study on New Sampling Plans and Optimal Integration with Proactive Maintenance in Production Systems

Sampling plans are statistical process control (SPC) tools used mainly in production processes. They are employed to control processes by monitoring the quality of produced products and alerting for necessary adjustments or maintenance. Sampling is used when an undesirable change (shift) in a process is unobservable and needs time to discover. Basically, the shift occurs when an assignable cause affects the process. Wrong setups, defective raw materials, degraded components are examples of assignable causes. The assignable cause causes a variable (or attribute) quality characteristic to shift from the desired state to an undesired state. The main concern of sampling is to observe a process shift quickly by signaling a true alarm, at which, maintenance is performed to restore the process to its normal operating conditions. While responsive maintenance is performed if a shift is detected, proactive maintenance such as age-replacement is integrated with the design of sampling. A sampling plan is designed economically or economically-statistically. An economical design does not assess the system performance, whereas the economic-statistical design includes constraints on system performance such as the average outgoing quality and the effective production rate. The objective of this dissertation is to study sampling plans by attributes. Two studies are conducted in this dissertation. In the first study, a sampling model is developed for attribute inspection in a multistage system with multiple assignable causes that could propagate downstream. In the second study, an integrated model of sampling and maintenance with maintenance at the time of the false alarm is proposed. Most of the sampling plans are designed based on the occurrence of one assignable cause. Therefore, a sampling plan that allows two assignable causes to occur is developed in the first study. A multistage serial system of two unreliable machines with one assignable cause that could occur on each machine is assumed where the joint occurrence of assignable causes propagates the process\u27s shift to a higher value. As a result, the system state at any time is described by one in-control and three out-of-control states where the evolution from a state to another depends on the competencies between shifts. A stochastic methodology to model all competing scenarios is developed. This methodology forms a base that could be used if the number of machines and/or states increase. In the second study, an integrated model of sampling and scheduled maintenance is proposed. In addition to the two opportunities for maintenance at the true alarm and scheduled maintenance, an additional opportunity for preventive maintenance at the time of a false alarm is suggested. Since a false alarm could occur at any sampling time, preventive maintenance is assumed to increase with time. The effectiveness of the proposed model is compared to the effectiveness of separate models of scheduled maintenance and sampling. Inspired by the conducted studies, different topics of sampling and maintenance are proposed for future research. Two topics are suggested for integrating sampling with selective maintenance. The third topic is an extension of the first study where more than two shifts can occur simultaneously

Integrated production quality and condition-based maintenance optimisation for a stochastically deteriorating manufacturing system

This paper investigates the problem of optimally integrating production quality and condition-based maintenance in a stochastically deteriorating single- product, single-machine production system. Inspections are periodically performed on the system to assess its actual degradation status. The system is considered to be in ‘fail mode’ whenever its degradation level exceeds a predetermined threshold. The proportion of non-conforming items, those that are produced during the time interval where the degradation is beyond the specification threshold, are replaced either via overtime production or spot market purchases. To optimise preventive maintenance costs and at the same time reduce production of non-conforming items, the degradation of the system must be optimally monitored so that preventive maintenance is carried out at appropriate time intervals. In this paper, an integrated optimisation model is developed to determine the optimal inspection cycle and the degradation threshold level, beyond which preventive maintenance should be carried out, while minimising the sum of inspection and maintenance costs, in addition to the production of non-conforming items and inventory costs. An expression for the total expected cost rate over an infinite time horizon is developed and solution method for the resulting model is discussed. Numerical experiments are provided to illustrate the proposed approach

Imperfect Maintenance Models, from Theory to Practice

The role of maintenance in the industrial environment changed a lot in recent years, and today, it is a key function for long-term profitability in an organization. Many contributions were recently written by researchers on this topic. A lot of models were proposed to optimize maintenance activities while ensuring availability and high-quality requirements. In addition to the well-known classification of maintenance activities—preventive and corrective—in the last decades, a new classification emerged in the literature regarding the degree of system restoration after maintenance actions. Among them, the imperfect maintenance is one of the most studied maintenance types: it is defined as an action after which the system lies in a state somewhere between an “as good as new” state and its pre-maintenance condition “as bad as old.” Most of the industrial companies usually operate with imperfect maintenance actions, even if the awareness in actual industrial context is limited. On the practical definition side, in particular, there are some real situations of imperfect maintenance: three main specific cases were identified, both from literature analysis and from experience. Considering these three implementations of imperfect maintenance actions and the main models proposed in the literature, we illustrate how to identify the most suitable model for each real case