Models And Methods For Maintenance Decisions: Theory And Applications

Maintenance plays a key role in keeping availability and reliability levels of industrial equipment, weapons, and transportation facilities, etc. One of the main expenditure items for various manufacturing firms is maintenance cost. However, the importance of maintenance is often neglected, and maintenance is considered as a "necessary evil" by most plant managers. Being different from other production and management problems, researchers and practitioners pay little attention to maintenance. This may be one of the reasons that result in low maintenance efficiency in industry at present, and it is reported that one third of all maintenance costs is wasted as the result of unnecessary or improper maintenance activities. Some maintenance strategies including reliability centered maintenance and total productive maintenance are popular and highlighted by maintenance engineers at present. However, lacking quantitative methods and models, most of them are based on engineers' experience and qualitative analysis. As an interdisciplinary of reliability and operations, maintenance modeling and optimization (maintenance decision models) has been developed since 1960s, and it aims to derive optimal maintenance decisions based on quantitative analysis of revenue and expenditure associated with maintenance activities.Early approaches of maintenance modeling and optimization are designed for performing time-based preventive maintenance. Recently, condition-based maintenance is becoming popular with the development of sensors and computers. This dissertation focuses mainly on condition-based maintenance modeling and optimization, in which three condition-based maintenance and spare provisioning policies and one condition-based opportunistic maintenance policy are proposed for one-unit or multi-unit deteriorating systems. On the other hand, the problem of selection of optimum maintenance strategies for different equipment/components are also addressed. The main contents of this dissertation are outlined as follows,(1) A condition-based order-replacement policy is presented for a single-unit system, aiming to optimize the condition-based maintenance and the spare order management jointly. The deterioration of the unit is modeled using the theory of the gamma process, and it is inspected periodically. The analytical modeling of the condition-based order-replacement policy is presented in detail for evaluating cost rate, reliability and availability. The policy is optimized by genetic algorithms, deriving the optimal inspection interval, ordering threshold, and preventive replacement threshold. Numerical examples illustrate the relation between optimization criteria, and the influences of the lead time of the spare order over the different performance criteria.(2) Considering the units that deteriorate continuously, it is found that the deterioration level just when the unit failure occurs, termed deterioration to failure, is uncertain. Therefore, the condition-based reliability is proposed in order to characterize various and uncertain deterioration levels when unit failure occurs. The theory of condition-based reliability is applied to maintenance optimization of haul truck motors, indicating that the condition-based reliability is able to characterize different reliability variations of different equipment with different deterioration histories.(3) Two new policies, referred to as the condition-based replacement and spare provisioning policies, are proposed for deteriorating systems with a number of identical and independent units. They combines the condition-based replacement policy with periodical inspections and the (S, s) type inventory policy, where S is the maximum stock level and s is the reorder level. The deterioration level of each unit under the two policies are described by Gamma process and discrete-time Markov chain, respectively. The related simulation models are developed for the system operation under the proposed condition-based replacement and spare provisioning policies. Thus, via the simulation method, the decision variables of the policies can be jointly optimized for minimizing the cost rate. The case studies are given, showing the procedure of applying the proposed policies to optimizing the maintenance scheme of haul truck motors at a mine site based on oil inspections, and proving beneficial for plant maintenance managers to reduce maintenance cost.(4) A new condition-based and opportunistic maintenance policy is given for multi-unit systems (e. g. a generating unit). Under this maintenance policy, the deterioration processes of equipment are discrete and modeled using continuous-time Markov chains, and maintenance decisions are given based on deterioration states of equipment. The Monte Carlo simulation model of the maintenance policy is also given in detail. By a numerical example, the application of the proposed maintenance policy to the optimization of condition-based maintenance in a thermal power plant is illustrated.(5) The problem of evaluating different maintenance strategies for different equipment/components is addressed. To deal with the uncertain judgment of decision makers, a fuzzy modification of the analytic hierarchy process (AHP) method is applied, where uncertain and imprecise judgments of decision makers are translated into fuzzy numbers. In order to avoid the fuzzy priority calculation and fuzzy ranking procedures in the traditional fuzzy AHP methods, a new fuzzy prioritization method is proposed. This fuzzy prioritization method can derive crisp priorities from a consistent or inconsistent fuzzy judgment matrix by solving an optimization problem with non-linear constraints. An example of selection of maintenance strategies in a power plant with the application of the proposed fuzzy AHP method is given.The conclusions and future directions are given at the end of the dissertation.