2023 | Buch

# Selective Maintenance Modelling and Optimization

## Basic Methods and Some Recent Advances

verfasst von: Yu Liu, Hong-Zhong Huang, Tao Jiang

Buchreihe : Springer Series in Reliability Engineering

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### Über dieses Buch

This book is a detailed introduction to selective maintenance and updates readers on recent advances in this field, emphasizing mathematical formulation and optimization techniques. The book is useful for reliability engineers and managers engaged in the practice of reliability engineering and maintenance management. It also provides references that will lead to further studies at the end of each chapter. This book is a reference for researchers in reliability and maintenance and can be used as an advanced text for students.

### Inhaltsverzeichnis

##### Chapter 1. Introduction
Abstract
Maintenance is an essential activity and a critical element in the lifecycle management of engineered systems. Maintenance optimization aims to determine the optimum maintenance tasks that minimize system downtime with the lowest possible costs or to maximize system reliability/availability under cost constraints. Selective maintenance aims to select only a subset of feasible maintenance actions to be performed for a repairable system owing to limited maintenance resources. It has been extensively studied over the past decade. This chapter provides a systematic review that focuses on maintenance optimization, particularly the selective maintenance optimization problem. First, an overview of maintenance optimization is provided. Maintenance optimization is distinguished from seven different criteria: maintenance paradigms, system degradation characteristics, maintenance efficiencies, inspection strategies, multi-component systems with dependencies, maintenance objectives, and optimization algorithms. Second, we conduct a deep literature review of selective maintenance problems from the perspectives of system modelling, efficiency of maintenance actions, constraints of maintenance resources, mission characteristics, operating environment, and optimization algorithms.
Yu Liu, Hong-Zhong Huang, Tao Jiang
##### Chapter 2. Basic Selective Maintenance Model
Abstract
In many industrial and military scenarios, a repairable system comprising a number of components is required to execute a sequence of missions, and the system can be repaired during breaks between two adjacent missions. Owing to limited maintenance resources (e.g., budget, time, and manpower), performing all desirable maintenance actions for all aged or failed components during the break can be costly. Using a selective maintenance strategy, a subset of feasible maintenance actions can be identified for a repairable system to improve the probability of the system successfully completing the next mission. This section introduced a basic mathematical model of selective maintenance. The system and components were assumed to be binary-state. During each break, multiple optional maintenance actions, from minimal corrective repairs to perfect preventive replacements, can be selected for each component. To maximize the probability of the repaired system successfully completing a future mission under the constraints of maintenance time and/or budget, three basic selective maintenance models were formulated. An illustrative example was provided to demonstrate the effectiveness of selective maintenance models.
Yu Liu, Hong-Zhong Huang, Tao Jiang
##### Chapter 3. Selective Maintenance for Multi-state Systems Under Imperfect Maintenance
Abstract
With the increasing complexity of modern advanced engineering systems, traditional selective maintenance strategies, which are simply designed for binary-state systems, cannot characterize the multi-state nature of systems in many engineering scenarios. Conversely, many maintenance activities can only restore failed/aged components to the condition somewhere between “as good as new” and “as bad as old,” and this is called imperfect maintenance. This chapter introduces a selective maintenance model for multi-state systems with binary-capacitated components under imperfect maintenance. The Kijima type-II age reduction model was utilized to quantify the maintenance efficiency of imperfect maintenance actions. The universal generating function method was used to evaluate the probability of a system successfully completing the next mission. Two illustrative examples were presented to demonstrate the effectiveness of the proposed method.
Yu Liu, Hong-Zhong Huang, Tao Jiang
Abstract
The load under which a system operates has a significant impact on failure behaviors of systems and their components. System reliability can be improved with not only an optimal maintenance strategy but also an optimal load distribution among components. This chapter proposes an approach to address the load distribution problem for multi-state systems using a selective maintenance strategy. A joint optimization model was formulated to optimize load distribution and allocation of limited maintenance budget to maximize the probability of the system successfully completing the next mission. A genetic algorithm was employed to solve the optimization problem. The results indicated that the proposed method achieves better results than traditional methods without considering load distribution.
Yu Liu, Hong-Zhong Huang, Tao Jiang
##### Chapter 5. Selective Maintenance under Stochastic Time Durations of Breaks and Maintenance Actions
Abstract
Most traditional selective maintenance studies assume that the time durations of breaks and maintenance actions are pre-specified. Such an assumption for simplicity purpose may, however, not always hold in many industrial and military applications, and the maintenance time duration and the start time of the next mission may not be exactly known in advance. This chapter introduced a selective maintenance model under the stochastic time durations of breaks and maintenance actions. Due to these time duration uncertainties, the maintenance sequence in a break indeed affect the completion of the selected maintenance actions. The probability of a system successfully completing the next mission was evaluated based on the distribution of the number of completed maintenance actions, and the saddlepoint approximation was implemented to facilitate the computation of the involved multi-dimensional convolution. A tailored ant colony optimization algorithm was developed to solve the resulting combinational optimization problem in the cases of large-scale systems. A four-component system and a multi-state coal transportation system, which were respectively solved by an enumeration method and the tailored ant colony optimization algorithm, were exemplified in this chapter.
Yu Liu, Hong-Zhong Huang, Tao Jiang
##### Chapter 6. Robust Selective Maintenance under Imperfect Observations
Abstract
The traditional selective maintenance optimization models have been developed on the premise that the condition of components can be accurately inspected. Nevertheless, this basic assumption may not always hold due to the limited inspection ability and accuracy. This chapter develops a robust selective maintenance optimization model involving the uncertainty produced by imperfect observations. A multi-objective optimization model was formulated with the aims of maximizing the expectation and simultaneously minimizing the variance of a system successfully completing the next mission. Consequently, a set of non-dominated solutions can be identified. Two illustrative examples were presented to validate the advantages of the proposed robust selective optimization model.
Yu Liu, Hong-Zhong Huang, Tao Jiang
##### Chapter 7. Selective Maintenance and Inspection Optimization for Partially Observable Systems
Abstract
Selective maintenance has been extensively investigated based on the premise that all component states after the last mission are precisely known in advance. However, in industrial scenarios, the inspection activities have to be conducted to identify component states, which might share the same resources with maintenance. Due to the limited maintenance effectiveness and inspection accuracy, both maintenance and inspection may be subject to uncertainty. To optimize the selective maintenance and inspection optimization for partially observable systems, a finite-horizon mixed observability Markov decision process (MOMDP) model was introduced when component states were partially observable while the remaining time resources were fully observable. In the MOMDP model, multiple optional maintenance and inspection actions can be dynamically executed during a break based on the state distribution of all components and the remaining time resources. Two illustrative examples were given to demonstrate the effectiveness of the proposed method.
Yu Liu, Hong-Zhong Huang, Tao Jiang
##### Chapter 8. Selective Maintenance for Systems Operating Multiple Consecutive Missions
Abstract
Most traditional selective maintenance optimization models only determined an optimal maintenance strategy for one single break. Nevertheless, engineering systems often intend to operate multiple consecutive missions, which necessities holistic maintenance decision-making for all breaks. This chapter developed a selective maintenance optimization model for systems that operate multiple consecutive missions. The uncertainties associated with the time duration of each future mission and the working time of each component in each future mission were considered. A max-min selective maintenance optimization model was formulated to determine the optimal maintenance activities for all breaks. Two illustrative examples were presented to demonstrate the advantages of the proposed selective maintenance optimization model.
Yu Liu, Hong-Zhong Huang, Tao Jiang
##### Chapter 9. Dynamic Selective Maintenance for Multi-state Systems Operating Multiple Consecutive Missions
Abstract
For a system operating multiple consecutive missions, the condition of each component can be inspected at the end of each mission. Therefore, the selective maintenance strategy needs to be dynamically determined given the condition of components, remaining maintenance resources, and the characteristics of future missions. This chapter develops a dynamic selective maintenance model for multi-state systems operating multiple consecutive missions. The maintenance actions are dynamically determined at the beginning of each break, so as to maximize the expected number of the successes of future missions. The sequential decision problem was formulated as a Markov decision process with a mixed integer-discrete-continuous state space. To mitigate the “curse of dimensionality,” a deep reinforcement learning (DRL) algorithm based on actor-critic framework was proposed to solve the problem. A postprocess was then utilized to search for the optimal maintenance actions in a constrained large-scale action space. Two illustrative examples were given to examine the effectiveness of the proposed dynamic selective maintenance model.
Yu Liu, Hong-Zhong Huang, Tao Jiang
##### Backmatter
Titel
Selective Maintenance Modelling and Optimization
verfasst von
Yu Liu
Hong-Zhong Huang
Tao Jiang