Reliability Modeling and Maintenance Policy Optimization for Deteriorating System Under Random Shock

In this chapter maintenance concept is defined and maintenance types are classified with regard to implementing maintenance policies toward preventive maintenance. Especially achieving planned maintenance policies toward preventive (planned) maintenance and condition based maintenance policies toward predictive maintenance is taken into consideration primarily. Maintenance concept is defined and maintenance types are classified. Due care is given for ‘Preventive Maintenance’ in this chapter. In general ‘Maintenance’ term could be defined as; The integration of all possible technical and administrative actions, including planning, supervising, monitoring and controlling toward retaining an item, a system, a machine to restore their original functional state in which they can perform the intended functions. In addition maintenance, include protective and corrective actions to keep the plant operational system in intended conditions or to maintain the acceptable manufacturing conditions. Optimum maintenance policies aims to sustain system reliability and robustness within minimum cost. In line with the progress of industry, increase in the system, material and manpower costs, increasing demand for robustness and the complex structure of the machines increases also the importance of maintenance policies. Maintenance types could be divided into two main parts namely; 1. Preventive Maintenance, 2. Corrective Maintenance. Preventive Maintenance is also classified into following sub groups; 1.a- Planned Maintenance, 1.b- Predictive Maintenance, 1.c- Advanced Maintenance Implementations; 1.c.1 Reliability Centered Maintenance 1.c.2 Risk Based Maintenance. All of these maintenance types elaborated with relevant figures in the chapter. In this chapter Comparison of Planned and Unplanned (corrective) Maintenance (With regard to transaction and output) is defined with a table. Additionally, Comparison of Planned and Unplanned (corrective) Maintenance (With regard to infra structure) is also tabulated. ‘Benefits of Preventive & Predictive Maintenance’ and ‘Predictive Maintenance Methods’ are defined with relevant descriptive figures in the chapter. For Corrective Maintenance, basic definitions and corrective maintenance steps, types of corrective Maintenance, improvement strategies in corrective maintenance effectiveness are also given. In the final part of ‘Results and conclusions’ expected and verified benefits of implementing maintenance policies for planned and predictive maintenance are explained. Comparisons in some maintenance policies is given.

Jet engines are critical assets in aircraft, and their availability is crucial in the modern aircraft industry. Therefore, their maintenance scheduling is one of the major tasks an airline has to make during an engine’s lifetime. A proper engine maintenance schedule can significantly reduce maintenance costs without compromising the aircraft's reliability and safety. Different maintenance scheduling approaches have been used for jet engines, such as corrective, preventive, and predictive maintenance strategies. Regarding the safety demands in aircraft industries, preventive maintenance is a frequent maintenance method for jet engines. However, preventive maintenance schedules are often use fixed maintenance intervals, which is usually suboptimal. This paper focuses on minimizing a jet engine's overall maintenance cost by optimizing its preventive maintenance schedule based on an engine’s comprehensive reliability model. A hierarchical optimization framework including the golden section search and genetic algorithms is applied to find the optimal set of preventive maintenance number and their times and the components to be replaced at those times during the jet engine's overall lifetime. The Monte Carlo simulation is used to estimate the engine’s failure times using their lifetime distributions from the reliability model. The estimated failure times are then used to determine the engine's overall corrective and preventive maintenance costs during its lifetime. Finally, an optimal preventive maintenance schedule is proposed for an RB 211 jet engine using the presented method. In the end, comparing the proposed method's overall maintenance cost with two other maintenance methods demonstrates the proposed schedule's effectiveness. The method presented in this paper is generic, and it can be used for other similar engineering systems.

An oil and gas company has 2,268 oil and gas wells. Well Barrier Element (WBE) is installed in a well to protect human, prevent asset damage and minimize harm to the environment. The primary WBE component is Surface Controlled Subsurface Safety Valve (SCSSV). The secondary WBE component is Christmas Tree Valves that consist of four valves i.e. Lower Master Valve (LMV), Upper Master Valve (UMV), Swab Valve (SV) and Wing Valve (WV). Current practice on WBE Preventive Maintenance (PM) program is conducted by considering the suggested schedule as stated on manual. Corrective Maintenance (CM) program is conducted when the component fails unexpectedly. Both PM and CM need cost and may cause production loss. This paper attempts to analyze the failure data and reliability based on historical data. Optimal PM interval is determined in order to minimize the total cost of maintenance per unit time. The optimal PM interval for SCSSV is 730 days, LMV is 985 days, UMV is 910 days, SV is 900 days and WV is 780 days. In average of all components, the cost reduction by implementing the suggested interval is 52%, while the reliability is improved by 4% and the availability is increased by 5%.

Shutting down a power plant for any reason is a costly process. Loss of production revenues from one unit can exceed millions of dollars per day. It is therefore good practice to keep outages as short and efficient as possible. A major component of planned outages is Preventive Maintenance (PM). It is important that PM be applied only where needed. Unnecessary PM is costly, not only in terms of labor and material costs, but in additional lost time and production. Further, there are cases where PM is contraindicated by standard reliability models, i.e. components with decreasing failure rates. This can paradoxically introduce failures, incurring additional repair costs and production loss. The risk management group at the South Texas Project Electric Generating Station (STPEGS) has successfully developed software to optimize PM schedules by minimizing total costs and risks associated with corrective maintenance (CM) and PM. The optimum PM interval is determined as a multiple of outage cycles. Components are evaluated based on maintenance type, risk ranking, cost of performing CM at power, and regulatory or internal commitments. This information provided to systems engineering assists in determining whether to advance PM from an upcoming outage, or defer PM to a subsequent outage. Components with maintenance that can be performed online are outside of the scope of this paper and are not considered here.

An Efficient Jaya Algorithm for Joint Optimization of Preventive Maintenance and Quality Policy in Production Systems

Simultaneous control of production, preventive and corrective maintenance rates of a failure-prone manufacturing system

Two-stage maintenance of a production system with exponentially distributed On- and Off-periods

Optimal maintenance policy for a system subject to damage in a discrete time process

This paper proposes an approach to select the optimum maintenance policy for mining equipment using risk-based maintenance (RBM). RBM constitutes the process of risk assessment that identifies high-risk components of equipment, and analytic hierarchy process (AHP) that helps selecting an optimum maintenance policy for individual components. By applying RBM, three high-risk components of dragline such as reduction gearbox, bearing, and motor pinion shaft have been identified,and then AHP was used on these three components by considering four criteria, eight sub-criteria and four alternative maintenance policies (such as periodic, preventive, predictive and corrective maintenance). Local and global scores for these three components were calculated from the opinion of three experts of the case study mine. AHP global scores revealed that the optimum maintenance policy for reduction gearbox and motor pinion shaft was preventive maintenance, whereas for bearing it was periodic maintenance policy. Adoption of such maintenance planning is expected to reduce maintenance cost and downtime of the dragline.

BackgroundThe real-world application of maintenance in organisations brings together a number of maintenance policies in order to achieve the desired availability, efficiency and profitability. However, the literature mostly chooses a single maintenance policy, and so the decision process is not suited to the real conditions in the company to which it is applied. Our study takes a combination of maintenance policies as alternatives, and so conforms to the actual practice of maintenance in organisations. Furthermore, it introduces the possibility of including extra spare parts, or outsourcing maintenance policies. Although the selection of maintenance policies has been applied to many kinds of business and of machine, there is almost no instance of its application to hospitals, and it has never been applied to delivery systems for cytostatic drugs.MethodsThe model uses the fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), which is recognised as being highly suitable for solving group decision-making problems in a fuzzy environment. Fuzzy set theory is also considered to be more proficient than crisp numbers for handling the ambiguity, imprecisions, data scarcity, and uncertainty inherent in decisions made by human beings. The judgements required were obtained from a decision group comprising the heads of facilities maintenance, maintenance of medical equipment, health and safety at work, environment, and programming-admission. The group also included care staff; specifically, the heads of the main clinical services, and the medical supervisors. The model includes original criteria, such as Quality of health care, which measures impact on care as a function of mean availability of each alternative. It also considers Impact on hospital management via the criteria: Working environment in the organisation and Impact on health care; the former criterion measures equality among care services in the hospital, while the latter assesses the effect on regional health cover. The model was built using real data obtained from a state hospital in Spain. The model can also be easily applied to other national and international healthcare organisations, providing weights specific to the criteria. These are produced by a decision group from each healthcare organisation and the alternatives are updated in accordance with what is considered important in each hospital.ResultsThe results obtained from the model recommend changing the alternative that is currently in use, Corrective and Preventive Maintenance, to Corrective and Preventive Maintenance plus two spare hoods. This alternative would lead to an availability of 1 (the highest possible) in the systems for preparing personalised cytotoxic drugs, and so the quality of service is therefore very high. Additionally, it could offer services to all the users of the hospital, and also offer cover in the preparation of cytotoxic medicines to other hospitals in the catchment area.ConclusionsThe results suggest the possibility that improvements to the support and logistical systems, which include maintenance, traditionally held to have no effect on quality of care, may be key to improving care quality, but also in reducing risk to patients, care and non-care staff, and the environment.

Dynamic preventive maintenance strategy for MMC considering multi-term thermal cycles

In recent decades, philosophy behind maintenance has varied consistently due to the changes in complexity of designs, advances in automation and mechanization, adaptation to the fast growing market demand, commercial computation in the sectors, and environmental issues. In mid-forties, simplicity of designs, limited maintenance opportunities, and immaturity of trade culture made enough to perform only fix it when it broke approach, i.e. corrective maintenance, after failures. Last quarter of the 21th century made essential to constitute more conservative and preventive maintenance policies in order to ensure safety, reliability, and availability of systems with longer lifetime and cost effectiveness. Preventive maintenance can provide an economic saving more than 18% of operating cost of systems. In this basis, various stochastic models were proposed as a tool to constitute a maintenance policy to measure system availability and to obtain optimal maintenance periods. This paper presents a general perspective on common stochastic models in maintenance planning such as Homogenous Poisson Process, Non-Homogenous Poisson Process, and Imperfect Maintenance. The paper also introduces two common maintenance policies, block and age replacement policy, using these stochastic models.

The preventive maintenance (PM) is scheduled periodically to restore the system condition. However, PM activities cannot generally restore the system to as good as new condition but in an intermediate situation, which is called imperfect PM. The PM interval optimization is useful for improving system reliability. A popular approach to optimization interval is minimizing the maintenance costs. The goal of this work is to obtain optimal PM intervals that minimize expected cost considered PM imperfect, in other words we obtain the optimal PM intervals assuming that the PM action restores the system operating state to be somewhere between as good as new and as bad as old. The results are applied in a real data and the PM is maintained at a decreasing sequence of intervals, which is more practical since most systems need more frequent maintenance with increased usage and age. Furthermore consider a wrong PM perfect hypotheses increase the maintenance cost substantially.

Different type of maintenance problems have been investigated during the past decades. With the growth in complexity of modern systems, the cost of purchasing new systems becomes higher ever than before. Surprisingly, very little attention in the literature has been paid to the failure replacement in these preventive maintenance models. However, in practice most systems incur increasingly expensive down time costs when the systems suffer the unexpected failures. The major changes to the standard maintenance model in this research are the addition of a failure replacement cost for a failed system and the reliability limit for which system has to be replaced. The overall objective of this research is to propose an efficient algorithm to obtain the optimal maintenance policy. The optimal preventive maintenance policy is investigated with incorporating the costs of minimal repair, imperfect preventive maintenance, failure replacement and preventive replacement. The specific maintenance actions for a repairable system are when to perform an imperfect preventive maintenance or to replace the system while the system is in the operating condition. In another case, the specific maintenance actions for a repairable system are whether to replace the system, or to perform a minimal repair when the system fails. The number of imperfect preventive maintenance and the control limits of the optimal maintenance policy are calculated under the expected average cost rate criterion.

Distribution systems are among the most complicated sectors of power systems. Unlike Generation and Transmission, most challenges in the distribution sector reflect the operational complexity rather than technological problems, and maintenance is crucial here. Transformers, vital equipment of every power system, require a fair amount of maintenance. Preventive-Maintenance is proven effective in improving the continuity of service and reliability of customers, but it is also proven costly. The complexity of maintenance modelling and optimization has forced utility companies to carryon with time-based maintenance with a lower performance in the executing process and results. In this article, we propose a comprehensive model to cover all possible states a transformer can experience, covering Preventive Maintenance (PM), the Corrective Maintenance (CM), and their required processes. Using discrete-time Markov-chain, we estimate the amount of time a transformer spends in each state, and therefore, calculate the required crews for each maintenance type. Using the calculated PM and CM crews, we define utility companies' cost function, reflecting the ENS (Energy not supplied), PM, and CM cost. Using this cost function and Genetic-Algorithm we try to allocate the PM and CM actions to minimize the total cost of utilities; this estimation can give Planners an upper hand in their cost-benefit and resource evaluation analysis.