Simultaneous scheduling of preventive system maintenance and of the maintenance workshop

While a system operates, its components deteriorate and in order for the system to remain operational, maintenance of its components is required. Preventive maintenance (PM) is performed so that component failure is avoided. This research aims at scheduling PM activities for a multi-component system within a finite horizon. The system to be maintained possesses positive economic dependencies, meaning that each time any component maintenance activity is performed, a common set-up cost is generated. Each component PM activity generates a cost, including replacement, service, and spare parts costs. We start from a 0-1 mixed integer linear optimization model of the PM scheduling problem with interval costs, which is to schedule PM of the components of a system over a finite and discretized time horizon, given common set-up costs and component costs, of which the latter vary with the maintenance interval. We extend the PMSPIC model to incorporate the flow of components through the maintenance/repair workshop, including stocks of spare components, both the components that require repair and the repaired ones. Our resulting model is a tight integration of the PM and the maintenance workshop scheduling. We investigate two different contract types between stakeholders, present and analyze preliminary numerical results obtained

A condition-based opportunistic maintenance policy integrated with energy efficiency for two-component parallel systems

Purpose: In order to improve the energy utilization and achieve sustainable development, this paper integrates energy efficiency into condition-based maintenance(CBM) decision-making for two-component parallel systems. The objective is to obtain the optimal maintenance policy by minimizing total cost. Design/methodology/approach: Based on energy efficiency, the paper considers the economic dependence between the two components to take opportunistic maintenance. Specifically, the objective function consists of traditional maintenance cost and energy cost incurred by energy consumption of components. In order to assess the performance of the proposed new maintenance policy, the paper uses Monte-Carlo method to evaluate the total cost and find the optimal maintenance policy. Findings: Simulation results indicate that the new maintenance policy is superior to the classical condition-based opportunistic maintenance policy in terms of total economic costs. Originality/value: For two-component parallel systems, previous researches usually simply establish a condition-based opportunistic maintenance model based on real deterioration data, but ignore energy consumption, energy efficiency (EE) and their contributions of sustainable development. This paper creatively takes energy efficiency into condition-based maintenance(CBM) decision-making process, and proposes a new condition-based opportunistic maintenance policy by using energy efficiency indicator(EEI).Peer Reviewe

Evaluasi Time based Maintenance (TBM) dalam Rangka Menurunkan Biaya Maintenance

Biaya perawatan menyumbang cukup besar pada biaya produksi, hal tersebut menuntut perusahaan untuk memilih strategi perawatan yang tepat. Selain itu, perkembangan teknologi yang cukup pesat menjadikan perawatan lebih mudah dan efektif untuk dilakukan dengan condition based maintenance (CBM). Strategi tersebut merupakan tindakan perawatan berdasarkan data kondisi dan historis mesin. Penerapan strategi CBM merupakan suatu alternative pilihan dalam evaluasi TBM tetapi membutuhkan biaya yang cukup tinggi untuk alat khusus dan juga adanya monitoring baik secara periodic maupun continuously. Apakah investasi mesin untuk CBM menyebabkan penurunan biaya perawatan atau malah sebaliknya penggunaan perawatan tradisional berdasarkan data historis (Time Based Maintenance, TBM) membutuhkan biaya yang lebih rendah dibandingkan penerapan strategi CBM. Objek penelitian ini adalah perusahaan yang bergerak dibidang produksi sarung tangan rajut. Terdapat beberapa proses yang ada termasuk pengrajutan, pengobrasan, dotting dan pengepakan. Mesin rajut merupakan mesin yang krusial dimana merupakan proses awal dari pembuatan sarung tangan. Beberapa perawatan yang telah dilakukan berupa perawatan preventive untuk pemberian oli dan perawatan corrective ketika terdapat produk cacat untuk penggantian jarum. Banyaknya sarung tangan rajut yang cacat karena adanya jarum yang patah sehingga mesin rajut tetap memproduksi produk yang cacat. Evaluasi TBM yang diaplikasikan pada mesin rajut untuk mendeteksi produk yang cacat menggunakan kamera webcam dengan membandingkan image processing produk yang baik dengan produk yang cacat. Biaya untuk TBM (existing) adalah preventive costs, repair cost, labor cost dan consequences of failure. Sementara untuk, biaya untuk evaluasi TBM adalah capital investment and annual cost of condition monitoring. Penelitian ini akan berfokus pada mesin rajut yang ada di perusahaan dan mengevaluasi TBM dalam rangka menurungkan biaya maintenance yang selanjutnya akan dipilih biaya yang lebih murah antara TBM (Existing) dengan evaluasi TBM berdasarkan nilai sekarang dengan pertimbangan economic life dan discount rate. ========================================================================================================= Cost of maintenance contribute substantially to production costs, requiring companies to choose appropriate maintenance strategies. In addition, rapid technological developments make maintenance easier and more effective to do with condition based maintenance (CBM). The strategy is a maintenance that measure based on the machine's condition and historical data. Implementation of CBM strategy is an alternative choice in TBM evaluation but it requires high cost for special tools and also monitoring both periodically and continuously. Whether investment of machine for CBM decrease maintenance costs or otherwise the use of time based maintenance (TBM) cheaper than CBM strategy. The object of this research is a company engaged in the production of knit gloves. There are several existing processes including knitting, hemming, dotting and packing. Knitting machine is a crucial machine which is the initial process of making gloves. Some maintenance have been done in the form of preventive maintenance for giving oil and corrective maintenance when there is a defective product for needle replacement. The number of knitting gloves that are deformed due to the needle is broken so that the knitting machine still produce defective products. Evaluation of TBM applied to knitting machines to detect defective products using webcam cameras by comparing good product image processing with defective products. The costs for TBM (existing) are preventive costs, repair costs, labor costs and consequences of failure. While for, the cost for evaluation of TBM is capital investment and annual cost of condition monitoring. This research will focus on the existing knitting machines in the company and evaluate TBM in order to reduce maintenance costs which will then be chosen cheaper cost between TBM (Existing) and evaluation of TBM based on present value with economic life consideration and discount rate

Integrated optimization of maintenance interventions and spare part selection for a partially observable multi-component system

Advanced technical systems are typically composed of multiple critical components whose failure cause a system failure. Often, it is not technically or economically possible to install sensors dedicated to each component, which means that the exact condition of each component cannot be monitored, but a system level failure or defect can be observed. The service provider then needs to implement a condition based maintenance policy that is based on partial information on the systems condition. Furthermore, when the service provider decides to service the system, (s)he also needs to decide which spare part(s) to bring along in order to avoid emergency shipments and part returns. We model this problem as an infinite horizon partially observable Markov decision process. In a set of numerical experiments, we first compare the optimal policy with preventive and corrective maintenance policies: The optimal policy leads on average to a 28% and 15% cost decrease, respectively. Second, we investigate the value of having full information, i.e., sensors dedicated to each component: This leads on average to a 13% cost decrease compared to the case with partial information. Interestingly, having full information is more valuable for cheaper, less reliable components than for more expensive, more reliable components

Modelo multiobjetivo para la selección de estrategias óptimas de mantenimiento en sistemas multicomponentes: una aplicación en líneas de transmisión de energía eléctrica

A multi-objective model is proposed for defining optimal maintenance strategies, in systems composed of several interconnected elements. The optimal maintenance strategies derived correspond to a set of efficient actions, focused on maximizing the reliability of the system, and minimizing the associated costs. Optimization is carried out by using evolutionary algorithms type NSGA-II. For the evaluation of the system reliability, a procedure based on Monte Carlo simulation is used, which allows analyzing systems with different performance functions and for component configurations different from the classical ones (series, parallel, k-out-of-N). The proposal is applied to assess electrical power system components, specifically the insulator chains of the transmission lines. Several scenarios illustrate the proposed model. The strategies selected by the model prioritize the most important elements based on costs and/or maintenance. These strategies make up an approximate Pareto front, in which the decision-maker can choose the most suitable strategy according to their interests.En este artículo se formula un modelo multiobjetivo para seleccionar estrategias de mantenimiento óptimas en sistemas formados por varios elementos interconectados. Las aquí planteadas corresponden al conjunto de acciones eficientes, centradas en maximizar la confiabilidad del sistema y, a su vez, minimizar los costos asociados. La optimización se realiza mediante el uso de algoritmos evolutivos tipo NSGA-II. Para evaluar la confiabilidad del sistema se utiliza un procedimiento basado en simulación de Monte Carlo, que permite analizar sistemas con distintas funciones de desempeño y para configuraciones de componentes diferentes a las clásicas (serie, paralelo, k-out-of-N). La propuesta se analiza para los componentes de un sistema eléctrico de potencia, específicamente las cadenas de aisladores de las líneas de transmisión, y varios escenarios de cálculo. Las estrategias seleccionadas por el modelo priorizan los elementos más importantes, según costo o mantenimiento, y conforman un frente de Pareto aproximado donde el decisor puede seleccionar la más adecuada, de acuerdo con sus intereses

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

Algorithmic Strategy for Simultaneous Optimization of Design and Maintenance of Multi-Component Industrial Systems

This article describes a new approach to simultaneous optimization of design and maintenance of large-scale multi-component industrial systems. This approach, in a form of an algorithm, aims to help designers in the search for solutions by characterizing the components and their architecture including maintenance issues. The aim is to improve the performance of the industrial systems by maximizing the Total Operational Reliability (TOR) at the lowest Life Cycle Cost (LCC). In the case of this research, the term "design" refers to the reliability properties of the components, possible redundancies, faulty component accessibility, and the ability to improve the component real-time monitoring architecture. The term “maintenance” refers to maintenance plan adapted to the opportunistic dynamic maintenance plan. Simultaneous optimization of design and maintenance is achieved by a two-level hybrid algorithm using evolutionary (genetic) algorithms. The first level identifies the optimal design solutions calculated relative to the TOR and the LCC. The second proposes a dynamic maintenance plan that maximizes the reliability of the system throughout its operating life

Evaluating resource sharing for offshore wind farm maintenance:The case of jack-up vessels

Offshore wind energy is recognised globally as a viable alternative to finite energy sources. However, large cost reductions are still needed, particularly in the Operations & Maintenance (O&M) phase, which currently accounts for about 30% of the cost of offshore wind. For large component replacements, a jack-up vessel is often leased from the spot market, resulting in high costs and low utilisation. These costs can be lowered when multiple wind farm service providers would share the resources needed to employ jack-up vessels. In this paper, we analyse two types of resource sharing, as an alternative to each service provider leasing its own vessel: (i) vessel purchasing and sharing and (ii) the combined use of vessel and harbour sharing. We design a simulation model and include stochastic processes such as weather patterns and component failures. Results show that cost benefits up to 45% can be achieved compared to a leasing policy, depending on the number of wind farm service providers involved and on the geographical distance between offshore wind farms. Moreover, it is shown that the jack-up vessel should not be fully utilised to minimise costs. The performance benefits of harbour sharing in addition to vessel sharing are generally small, but become more significant if the network faces considerable congestion. Results are illustrated using a case study based on a setting in the Western North Sea

Condition-based Inspection and Maintenance of Medical Devices

Inspection and Maintenance of medical devices are essential for modern health services, but the low availability of devices or unnecessary maintenance can cause major problems. A proper maintenance program can signi cantly reduce operational costs and increase device availability. For any maintenance program, two questions arise: 1) What kinds of devices should be included? and 2) How and when should they be inspected and maintained? This thesis proposes methods to solve those two problems. For the rst question, numerous classi cation and prioritization models have been suggested to evaluate medical devices, but most are empirical scoring systems, which can not be widely used. To build a generalized scoring system, we propose a risk level classi cation model. More speci cally, we select three important risk factors (Equipment function, Location of use and Frequency of use), then use provided data to nd the relationship between risk factors and risk levels. Four di erent classi cation models (Linear regression, Logistic regression, Classi cation tree and Random forest) are used to analyze the problem, and all of them are effective. For the second question, some inspection and maintenance models have been developed and widely used to assure the performance of medical devices. However, those models are restricted to a few speci c kind of problems. In contrast, our model provides a more comprehensive response to current maintenance problems in the healthcare industry, by introducing a condition-based multi-component inspection and maintenance model. We rst present a parameter estimation method to predict the deterioration rate of a system. We use provided data and expectation-maximization algorithm to estimate the transition matrix of system conditions. Then, we use Markov decision processes to solve the decision model, which consists of two decisions: the next inspection time and whether to repair the devices. The inspection interval is non-periodic in our model, and this flexibility of non-periodic inspection model can avoid unnecessary inspections. We use relative value iteration to nd the optimal inspection and maintenance strategies and the long-run average cost. Changing the parameter of cost and the structure of the system clarified the influence of these parameters. Our model achieves lower minimal average costs for complex systems than previous periodic inspection models

Clustering condition-based maintenance for systems with redundancy and economic dependencies

Systems that require maintenance typically consist of multiple components. In case of economic dependencies, maintaining several of these components simultaneously can be more cost efficient than performing maintenance on each component separately, while in case of redundancy, postponing maintenance on some failed components is possible without reducing the availability of the system. Condition-based maintenance (CBM) is known as a cost-minimizing strategy in which the maintenance actions are based on the actual condition of the different components. No research has been performed yet on clustering CBM tasks for systems with both economic dependencies and redundancy. We develop a dynamic programming model to find the optimal maintenance strategy for such systems, and show numerically that it can indeed considerably outperform previously considered policies (failure-based, age-based, block replacement, and more restricted (opportunistic) CBM policies). Moreover, our numerical investigation provides insights into the optimal policy structure