Optimal Preparedness Maintenance of Multi-Unit Systems With Imperfect Maintenance and Economic Dependence

Wang, Hongzhou; Izundu, Anthony E.; Pham, Hoang

doi:10.1142/9789812811868_0006

Cited by 11 publications

(7 citation statements)

References 12 publications

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“…Some systems can only accept maintenance actions before given tasks, that is, defects may be tested while the systems are in storage. This includes rocket engines, missiles, and so on [17]. Here, we only study the situation that maintenance actions can be taken at any time.…”

Section: Assumptionsmentioning

confidence: 99%

Multi-Criteria Decision Model for Imperfect Maintenance using Multi-Attribute Utility Theory

Zhao¹

2018

IJPE

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Many research works have been conducted in the preventive maintenance area since maintenance strategies have become more and more significant in industry and supply chain services. However, previous studies are mainly based on age maintenance policies and other multi-criteria approaches instead of Multi-Attribute Utility Theory (MAUT). There are some studies proposed by using MAUT, but they assume that the maintenances are perfect. This paper presents an imperfect maintenance model of a one-unit system to obtain an optimal inspection interval based on MAUT. The proposed model is designed to identify the systems' status by making a trade-off between cost attribute and reliability attribute. By taking decision makers' preferences into account, with the assumption of imperfect maintenance, the model receives a result of an optimal inspection interval. This model is applicable for equipment or systems that suffer graded failures and can be repaired at any time during the operation. A numerical application is given to illustrate that with consideration of decision-makers' priorities, the model can provide new solutions for imperfect maintenance interval optimization, which is a suggestion for future research.

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Section: Assumptionsmentioning

confidence: 99%

Multi-Criteria Decision Model for Imperfect Maintenance using Multi-Attribute Utility Theory

Zhao¹

2018

IJPE

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“…The selected approach highly conditions the adequacy of the maintenance strategy [33]. In the case of WTs, it is important to select one that takes into account the multi-component nature of the turbines [34,35].…”

Section: Background and Introductionmentioning

confidence: 99%

“…The WTs are composed of several subsystems with dependencies among them that can be classified as (i) economic, where the simultaneous performance of maintenance activities implies different economic consequences that implement them individually [36]; (ii) structural, where maintenance actions on one system may imply maintenance activities on others [37]; and (iii) stochastic, where the failure hazard of two different systems are not independent [38]. In this context, opportunistic maintenance policies are the most suitable, and therefore they have been widely researched [33].…”

Section: Background and Introductionmentioning

confidence: 99%

Framework for Managing Maintenance of Wind Farms Based on a Clustering Approach and Dynamic Opportunistic Maintenance

et al. 2019

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Energies 2019, 12, 2036 2 of 17 account for up to one quarter of the proportion of the LCoE, with 80% of this expenses directly attributed to maintenance [10][11][12].In view of the maintenance role in the LCoE and thus in the profitability of WFs, it is important to consider models for optimization of O&M plans and decisions [2]. The evolution of maintenance models and methodologies have kept pace with the constant technological evolution of Wind Turbines (WTs) [3]. According to [3], the goal of all the approaches and methodologies is determining the most adequate maintenance plan, the management of the resources, and the aspects related to Reliability, Availability, and Maintainability (RAM) of the WTs.Within such context, the WF operators are bound to develop new techniques and decision-support tools for optimal maintenance strategies, if they strive to maximize the profitability of the investment [8]. Accordingly, maintenance management discipline acquires a highly significant position since it provides a comprehensive perspective for the management of WTs, allowing for optimal maintenance strategies which reduce maintenance costs while maximizing availability [3,5].The decision-making process in the asset management field has been divided into strategic (long-term), tactic (medium-term), and operational (short-term) to achieve excellence in maintenance [13][14][15]. It is the purpose of the research in this paper to address the different aspects of the maintenance decision-making process. This is done by answering the research question of whether it is possible to achieve maintenance excellence for the lifecycle of WFs by a maintenance strategy which considers the different behaviors of the WTs and integrated business-related objectives.Aiming at providing an answer for the aforementioned research question, a technical framework for managing maintenance is proposed in the paper. The framework is a comprehensive proposal that considers different aspects regarding the maintenance of a WF. Within the possibilities offered by the current trend for big data, certain key aspects to create a failure database are integrated in the framework, which enables a clustering approach based on the failure behaviors of the different failure modes of each WT. This approach supports an opportunistic maintenance policy with dynamic thresholds that regard not only to the reliability of the assets but also business considerations. Besides, the framework also incorporates the strategic view through a Life-Cycle Cost (LCC) perspective integrated by means of a multi-objective optimization and supported by simulation techniques that provide valuable information to find an attractive trade-off between cost and performance.

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“…Gustavsson et al (2014) proposed a preventive maintenance (PM) scheduling over a finite and discretized time horizon based on the interval costs. Maintenance optimization models of multi-component systems are widely reviewed in by Dekker et al (1997), Nicolai and Dekker (2008), Wang (2002), Pham and Wang (1996), Wang and Pham (2006), Nowakowski and Werbińka (2009), and Wu and Zuo (2010). Bai and Pham (2006) have presented some results for multi-component systems on renewable full-service warranty policies.…”

Section: Introductionmentioning

confidence: 99%

An integrated strategy for fleet maintenance planning

Rawat

Lad

2017

JQME

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Purpose Conventionally, fleet maintenance decisions are made based on the level of repair (LOR) analysis. A general assumption made during LOR analysis is the consideration of the lifetime distribution with constant failure rate (CFR). However, industries do use preventive maintenance (PM) to extend the life of such components, which in turn may affect the LOR decisions such as repair/move/discard. The CFR assumption does not allow the consideration of effect of PM in LOR analysis. The purpose of this paper is to develop a more practical LOR analysis approach, considering the time-dependent failure rate (TDFR) of components and the effect of PM. Design/methodology/approach In the proposed methodology, first, a detailed life cycle model considering the effect of various parameters related to LOR and PM is developed. A simulation-based genetic algorithm approach is then used to obtain an integrated solution for LOR and PM schedule decisions. The model is also evaluated for the various cases of quality of maintenance measured in terms of degree of restoration. Findings The results, from the illustrative example for a multi-indenture and multi-echelon fleet maintenance network, show that the proposed integrated strategy leads to better LCC performance compare to the conventional approach. Additionally, it is identified that the degree of restoration also affects the PM schedule as well as LOR decisions of the fleet system. Therefore, consideration of TDFR is important to truly optimize the LOR decisions. The proposed approach can be applied to fleet of any equipment. Research limitations/implications The approach is illustrated using a hypothetical example of an industrial system. A more complex system structure in terms of number of machines, types of machines (identical vs non-identical), number of echelons, possible repair actions at various echelons, etc. may be present for a particular industrial case. However, the approach presented is generic and can be extended to any system. Moreover, the aim of the paper is to highlight the importance of the considering PM and quality of maintenance in LOR decision making. Originality/value To the best of the authors’ knowledge, this is the first work which considers the effect of PM and quality of maintenance on LOR analysis. Consideration of TDFR and imperfect maintenance while optimizing LOR decisions is a complex problem. Thus, the work is of high significance from the research point of view. Also, most of the real life fleet systems use PM to extend the life of the equipment. Thus, present paper is a more practical approach for LOR analysis of such systems.

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Optimal Preparedness Maintenance of Multi-Unit Systems With Imperfect Maintenance and Economic Dependence

Cited by 11 publications

References 12 publications

Multi-Criteria Decision Model for Imperfect Maintenance using Multi-Attribute Utility Theory

Multi-Criteria Decision Model for Imperfect Maintenance using Multi-Attribute Utility Theory

Framework for Managing Maintenance of Wind Farms Based on a Clustering Approach and Dynamic Opportunistic Maintenance

An integrated strategy for fleet maintenance planning

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