Importance Analysis and Maintenance Decisions of Complex Systems with Dependent Components

Description

This project aims to improve the effectiveness of complex system maintenance through innovative adaptation of ‘importance measures’. In particular, we examine the problem of effective prioritisation of maintenance resources by identifying the most critical components in a complex system to be selected for maintenance. This is of growing urgency for complex real-world systems such as power networks, traffic systems and infrastructure, where the asset owners are experiencing ever-tightening operational and maintenance budgets. Although ‘importance measures’ have been a topic of considerable interest in the reliability community, their use in supporting maintenance decisions for complex systems have been impeded due to some fundamental flaws. Firstly, in most complex systems, component failures and degradation are not independent, and these behaviours of a component are also influenced by other components in the system. Further, the availability of resources (e.g., personnel, spares) has a significant influence on the components to be selected for maintenance. These two issues – which are insufficiently explored in the extant academic literature in the development of ‘importance measures’ – is the focus of this proposal.The novelty of the proposed research is two-fold. First, this project will develop importance measures specifically designed for condition-based maintenance, incorporating the dynamic and stochastic nature of component degradation and also importance measures that explicitly considers the dependency between components in a complex system which is new to the existing literature and important for practical decision making. Second, we will develop a technique that exploits the new importance measure to optimise condition-based maintenance with the multiple options for intervention including opportunistic maintenance, especially a procedure based on importance measures that allow joint analysis of maintenance and spare parts optimisation.The proposed research project developed through our collaborative effort with the research group at Univ of Cambridge has clear practical impacts on industrial applications across different sectors. The maintenance-oriented importance measures developed through this project will guide the selection of components for maintenance so that the system performance can be improved in a more effective and cost-efficient manner. By facilitating the identification of the parameters that exert the most significant influence on system performance, asset managers can use these importance measures to make decisions that ensure their investment in maintenance delivers maximum value to the organisation.