A general inspection and opportunistic replacement policy for one-component systems of variable quality

We model the influence of opportunities in a hybrid inspection and replacement policy. The base policy has two phases: an initial inspection phase in which the system is replaced if found defective; and a later wear-out phase that terminates with replacement and during which there is no inspection. The efficacy of inspection is modelled using the delay time concept. Onto this base model, we introduce events that arise at random and offer opportunities for cost-efficient replacement, and we investigate the efficacy of additional opportunistic replacements within the policy. Furthermore, replacements are considered to be heterogeneous and of variable quality. This is a natural policy for heterogeneous systems. Our analysis suggests that a policy extension that allows opportunities to be utilised offers benefit, in terms of cost-efficiency. This benefit is significant compared to those offered by age-based inspection or preventive replacement. In addition, opportunistic replacement may simplify maintenance planning

Delay-time modelling of a critical system subject to random inspections

We model the inspection-maintenance of a critical system in which the execution of inspections is random. The models we develop are interesting because they mimic realities in which production is prioritised over maintenance, so that inspections might be impeded or they might be opportunistic. Random maintenance has been modelled by others but there is little in the literature that relates to inspection of a critical system. We suppose that the critical system can be good, defective or failed, and that failure impacts on production, so that a failure is immediately revealed, but a defect does not. A defect, if revealed at inspection, is a trigger for replacement. We compare the cost and reliability of random inspections with scheduled periodic inspections and discuss the implications for practice. Our results indicate that inspections that are performed opportunistically rather than scheduled periodically may offer an economic advantage provided opportunities are sufficiently frequent and convenient. A hybrid inspection and replacement policy, with inspections subject to impediments, is robust to departure from its inspection schedule. Keywords: Maintenance; reliability; random inspection; production; qualit

Condition-Based Production Planning:Adjusting Production Rates to Balance Output and Failure Risk

Problem Definition: Many production systems deteriorate over time as a result of load and stress caused by production. The deterioration rate of these systems typically depends on the production rate, implying that the equipment's deterioration rate can be controlled by adjusting the production rate. We introduce the use of condition monitoring to dynamically adjust the production rate to minimize maintenance costs and maximize production revenues. We study a single-unit system for which the next maintenance action is scheduled upfront. Academic/Practical Relevance: Condition-based maintenance decisions are frequently seen in the literature. However, in many real-life systems, maintenance planning has limited flexibility and cannot be done last minute. As an alternative, we are the first to propose using condition information to optimize the production rate, which is a more flexible short-term decision. Methodology: We derive structural optimality results from the analysis of deterministic deterioration processes. A Markov decision process formulation of the problem is used to obtain numerical results for stochastic deterioration processes. Results: The structure of the optimal policy strongly depends on the (convex or concave) relation between the production rate and the corresponding deterioration rate. Condition-based production rate decisions result in significant cost savings (by up to 50%), achieved by better balancing the failure risk and production output. For several systems a win-win scenario is observed, with both reduced failure risk and increased expected total production. Furthermore, condition-based production rates increase robustness and lead to more stable profits and production output. Managerial Implications: Using condition information to dynamically adjust production rates provides opportunities to improve the operational performance of systems with production-dependent deterioration

A review of asset management literature on multi-asset systems

This article gives an overview of the literature on asset management for multi-unit systems with an emphasis on two multi-asset categories: fleet (a system of homogeneous assets) and portfolio (a system of heterogeneous assets). As asset systems become more complicated, researchers have employed different terms to refer to their specific problems. With an objective to facilitate readers in searching conducive studies to their interests, this paper establishes a novel classification scheme for multi-unit systems in accordance with essential features such as diversity of assets and intervention options. Moreover, discerning differences in characteristics between cross-component and cross-asset interactions, we select three types of potential multi-component dependencies (performance, stochastic, and resource) and extend their notions to be applicable to multi-asset systems. The investigation into these dependencies enables the identification of problems that could exist in real industrial settings but are yet to be determined in academia. Ultimately, we delve into modelling approaches adopted by previous researchers. This comprehensive information allows us to offer the insights into the current trends in multi-asset maintenance. We expect that the output of this review paper will not only stress research gaps on multi-asset systems, but more importantly help systematise future studies on this aspect

İmalat sektöründe uygun bakım ve stratejisinin belirlenmesi için bir yöntem tasarımı ve uygulaması

06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.Anahtar kelimeler: Fırsatçı bakım, sürekli-proses tipi imalat, bulanık mantık, uzman sistem Bakım planlaması, imalat sistemlerinin verimliliği açısından önemli bir husustur. Etkin bir bakım planlaması, imalat sisteminin verimliliğinin yüksek olmasını sağlayacaktır. İmalat sisteminin veriminin artması, yüksek bir imalat miktarını da beraberinde getirecektir. Bununla beraber, özellikle sürekli-proses tipi imalat sistemlerinde bir makinenin arızalanması tüm sistemin çalışmasını olumsuz etkileyebilmektedir. Doğru bakım politikasının uygulanması bu tip imalat sistemlerinde büyük önem arz etmektedir. Bu çalışmada, bulanık uzman sistem tabanlı bir fırsatçı bakım politikası geliştirilmiştir. Geliştirilen bu yeni bakım politikasının performansı sürekli-proses tipi imalat sistemlerinde toplam çıktı miktarı göz önünde bulundurularak incelenmiştir. Çalışma kapsamında, bir çimento fabrikasının imalat süreci incelenmiş ve verileri kullanılmıştır. İmalat sisteminin modellenmesinde simülasyon yaklaşımından faydalanılmıştır. Deney tasarımı için Taguchi metodu kullanılmıştır. Çalışmanın sonunda simülasyon sonuçları varyans analizi kullanılarak değerlendirilmiştir ve önleyici bakım ve fırsatçı bakım politikaları bu sonuçlara göre karşılaştırılmıştır. Sonuçlar incelendiğinde, sürekli-proses tipi imalat sistemlerinde toplam çıktı miktarının maksimum olabilmesi için bakım politikası olarak %25 kontrol oranlı fırsatçı bakım politikası, arıza dağılımı için Weibull dağılımı parametrelerinden β’nın 4 olması, sistem uzunluğunun az olması, ara stok kapasitesinin yüksek olması ve arıza bakımı maliyetinin önleyici bakım maliyetine oranının küçük olması gerekmektedir. Bu sonuçlar, %25 kontrol oranına sahip fırsatçı bakım politikasının sürekli-proses tipi imalat sistemlerinde önleyici bakıma göre toplam çıktı miktarı açısından daha iyi bir performans verdiğini göstermektedir. DESIGN AND IMPLEMENTATION OF A METHOD FOR DETERMINING THE OPTIMUM MAINTENANCE POLICY IN MANUFACTURING SECTORKeywords: Opportunistic maintenance, continous-process type manufacturing system, fuzzy logic, expert system Maintenance planning is of crucial importance with regard to productivity of manufacturing systems. Effective maintenance planning ensures high productivity of manufacturing system. Increasing the efficiency of the manufacturing system will bring with it a high manufacturing amount. However, failure of a machine, especially in continuous-process type manufacturing systems, can adversely affect the operation of the entire system. The application of optimal maintenance policy is of crucial importance in these types of manufacturing systems. In this study, fuzzy expert system based opportunistic maintenance policy has been developed. Performance of this novel maintenance policy has been investigated for continous-process type manufacturing system in terms of output amount. In the scope of the study, the manufacturing process of a cement plant was examined and data were used. Simulation approach was used for modeling the manufacturing system. Taguchi method was used for design of experiment. At the end of the study, simulation results were evaluated by using variance of analysis and preventive maintenance and opportunistic maintenance policy were compared according to results. When the results are examined, it is seen that 25% control ratio opportunistic maintenance policy , β=4 for weibull distribution parameter, low system length, high buffer capacity, the small proportion of maintenance costs to preventive maintenance costs enable high output amount in continous-process type manufacturing systems. These results show that the 25% control ratio opportunistic maintenance policy provides better performance in terms of total output compared to preventive maintenance in continuous-process type manufacturing systems

Spare parts classification in industrial manufacturing using the dominance-based rough set approach

Classification is one of the critical issues in the operations management of spare parts. The issue of managing spare parts involves multiple criteria to be taken into consideration, and therefore, a number of approaches exists that consider criteria such as criticality, price, demand, lead time, and obsolescence, to name a few. In this paper, we first review proposals to deal with inventory control. We then propose a three-phase multicriteria classification framework for spare parts management using the dominance-based rough set approach (DRSA). In the first phase, a set of ‘if–then’ decision rules is generated from historical data using the DRSA. The generated rules are then validated in the second phase by using both the automated and manual approaches, including cross-validation and feedback assessments by the decision maker. The third and final phase is to classify an unseen set of spare parts in a real setting. The proposed approach has been successfully applied to data collected from a manufacturing company in China. The proposed framework was practically tested on different spare parts and, based on the feedback received from the industry experts, 96% of the spare parts were correctly classified. Furthermore, the cross-validation results show that the proposed approach significantly outperforms other well-known classification methods. The proposed approach has several important characteristics that distinguish it from existing ones: (i) it is a learning-set based analysis approach; (ii) it uses a powerful multicriteria classification method, namely the DRSA; (iii) it validates the generated decision rules with multiple strategies; and (iv) it actively involves the decision maker during all the steps of the decision making process

Managing the restoration of membranes in reverse osmosis desalination using a digital twin

This thesis studies degradation and restoration policies for a pressure vessel in a reverse osmosis (RO) desalination plant. In the study context, biofouling is the primary cause of the degradation of the RO membrane elements, amplified by seasonal algal blooms. This research developed a decision support system (DSS) for evaluating membrane restoration strategy. The engine of the DSS is a digital twin (DT), a virtual representation of wear (degradation) and restoration of membrane elements in a RO pressure vessel. The basis of the DT is a mathematical model that describes an RO pressure vessel as a novel multi-component system in which the hidden wear-states of individual elements (components) are quantified, and elements can be swapped or replaced. This contrasts with the contemporary presentation of a membrane system as a single system in the literature. The parameters of the model are estimated using statistical methods. The research approach is described in the context of a case study on the Carlsbad Desalination Plant in California. Results show a good fit between the observed and the modelled wear-states. Competing policies are compared based on risk, cost, downtime, and the number of stoppages. Projections indicate that a significant cost-saving can be achieved while not compromising the integrity of the plant. Alternative policies 11 and 12 showed better wear management than the current policy 10 of the maintenance company while reducing costs between $0.7 to$1.7 million for the next five years.The research in the thesis contributes toward maintenance modelling. New models of multivariate degradation and imperfect repair are presented. The research makes an important contribution to desalination and water treatment engineering, providing a unique membrane maintenance management approach currently absent from the literature. The thesis also contributes to the maintenance theory. It proposes a general approach for applying a decision support system (DSS) for maintenance requirements analysis, involving a digital twin (DT) for wear and repair projections when wear is stochastic, and repair effects are not immediately apparent. The essential elements of a DSS are discussed. This research encourages a dialogue between researchers of maintenance theory and modelling and practitioners of maintenance planning about decision support systems and digital twins that not only project the when but also evaluate the what in maintenance strategy. The presented concept of a DSS driven by a DT for maintenance requirement analysis has valuable practical implications, and the thesis, in discussing this concept, makes an essential contribution to the discussion about Industry 4.0, digital twins, and maintenance