Advanced Maintenance Modelling for Industrial Systems

Maintenance has an important role in modern economies and industries. Effective maintenance can improve system safety and reliability and reduce whole-life cycle costs of complex engineered systems. With the emergence of new technology and opportunities to collect system performance and condition data, it has become necessary to develop advanced methods for modelling maintenance of complex systems. Maintenance modelling allows balancing the cost of performing maintenance for a system against the losses incurred due to its performance loss. It provides decision makers with the knowledge and tools to enable them to reduce costs or/and keep system performance at a desired and safe level. Over the years, research in maintenance modelling has attracted considerable attention from both academy and industry and it is the main focus of this special issue, which contains a number of articles that are focussed on recent advancements in modelling maintenance for complex and industrial systems. The first paper by Tamssaouet et al. reviews literature in the areas of system-level prognostics and RUL estimation for multicomponent systems. Prognostics and Health Management approaches integrate fault detection, failure diagnostics, prognostics and maintenance decision support processes, and their effective usage can make large savings in asset management costs. Many studies focus on component-level prognostics, but their practical use can be enhanced only if system operators and maintenance managers can base their decisions on system-level parameters of complex system performance. Future challenges in this relatively recent research area conclude the paper. The second paper by Corset et al. proposes a stochastic model for imperfect condition-based maintenance. The degradation is modelled by a gamma process, and the condition-based maintenance policy with perfect corrective and imperfect preventive actions is proposed. The statistical inference of the model parameters is carried out, considering degradation data with imperfect maintenance. Finally, a sensitivity analysis shows how the whole lifecycle maintenance cost depends on the degradation and maintenance model parameters, and such information can support asset management decision-making processes. Ulansky and Raza in the third paper focus on imperfect inspections while modelling condition-based and preventive maintenance. The authors develop probabilistic indicators of imperfect inspections that can be used to describe correct and incorrect decisions. The effectiveness indicators of such maintenance are expressed in terms of operating costs, total error probability and a posterior probability of failure-free operation. The paper concludes with emphasising the importance of including time-dependency in the obtained probabilities of correct and incorrect decisions. A mathematical model of a hybrid maintenance policy is proposed in the fourth paper by Melo et al. Such a policy consists of combining periodic inspection, corrective and opportunistic maintenance activities, especially applicable to geographically remote assets such as wind farms. During the opportunistic phase, preventive replacement can be executed early if an opportunity arises, that is, while pre-planned maintenance is performed. In this case, it can be shown that remote systems with high logistics costs and restricted access may benefit from opportunistic maintenance policies. The fifth paper by Cavalcante et al. focusses on factors that can affect quality of inspections and maintenance, including disruptive external events, such as managerial and environmental factors, that can influence human performance. Inspection defects can cause significant increase in cost and increased likelihood of system failure. Therefore, more realistic maintenance models can be achieved through considering human behaviour and social aspects that influence it. The issue is concluded by a contribution from Sun et al. which proposes a life-cycle maintenance modelling approach, specific to aero engine fleet management. The framework takes account of different life-cycle phases, such as maintenance, repair and overhaul, as well as spare inventory, which are influenced by environmental and operating conditions. The approach is based on a simulation method, and it is demonstrated that the framework can be used for engine reliability assessment, maintenance cost prediction and spare inventory planning, and it can be used as a decision support aid in engine fleet maintenance management. The Guest Editors would like to thank all authors for their contributions in this special issue and the reviewers for their time in providing the authors with constructive feedback. We would also like to thank the Editor-inChief, Professor Terje Aven, and the editorial team who worked on the publication of this special issue.