Availability Analysis and Preventive Maintenance Planning for Systems with General Time Distributions

Significance 

System failures can be pre-empted by ensuring timely maintenance during the operating life of the system. Ideally, it is vital to implement an effective maintenance strategy to ensure a system’s availability uninterrupted. The simplest maintenance plans involve only corrective maintenance; i.e., maintenance is only performed upon an unexpected failure. In contrast, many preventive maintenance models have been studied to reduce the significant loss experienced during system failures. Of these, the steady-state availability is one of the most important performance measures for repairable systems. To further improve the steady-state availability approach, preventive maintenance models have been extensively studied. However, these models assume that the distributions of the system lifetime and maintenance duration are exponential. In practice, the probabilistic characteristics of different systems and maintenance actions are so broad making the exponential distribution an inappropriate model. A review of existing literature reveals that majority of existing preventive maintenance studies assume that preventive maintenance actions are carried out precisely at scheduled preventive maintenance times. However, in practice, preventive maintenance actions are usually carried out flexibly during a time window around scheduled preventive maintenance times due to practical reasons. To this end, various functions have been developed to evaluate the effectiveness of preventive maintenance polices. Most of them are based on cost-related and availability related criteria.

Generally, in real applications, both lifetime and maintenance durations can have different distribution types. It is therefore necessary to develop a steady-state availability model for systems with general distributions so that the optimal preventive maintenance plan can be determined to match the stochastic nature of such systems. Motivated by this, Beihang University researchers: Dr. Naichao Wang, Dr. Lin Ma and Dr. Boping Xiao, together with Professor Jiawen Hu from University of Electronic Science and Technology of China, and Professor Haitao Liao from University of Arkansas, investigated a preventive maintenance model that maximizes the steady-state availability of a system with general distributions, where preventive maintenance actions are carried out flexibly during a time window around scheduled preventive maintenance times. The researchers sought to determine the optimal preventive maintenance strategy that maximizes the steady-state availability of a system involving general probability distributions. Their work is currently published in the research journal, Reliability Engineering and System Safety.

In their approach, the preventive maintenance actions were scheduled periodically, and a replacement was carried out to restore the system back to its good as-new state after a certain number of imperfect maintenance actions or upon a failure. The researchers developed a state-transition equation using the supplementary variable method, and the steady-state availability was obtained based on the stationary distribution.

Remarkably, numerical examples demonstrated that a flexible preventive maintenance time could influence the optimal decision variables, which were determined by the specific transition rate function and duration of the time window. Moreover, they proved the existence of the optimal combination of the number of imperfect maintenance actions before each replacement and scheduled preventive maintenance interval, in addition to presenting an algorithm for solving the optimization problem.

In summary, the study presented an in-depth assessment of a preventive maintenance planning model that maximizes the steady-state availability of a system with general lifetime and maintenance duration distributions. In the presented model, preventive maintenance actions were carried out flexibly during a time window around scheduled epochs, where several imperfect maintenance actions were carried out before each replacement. The proposed model has many applications in practice because it is capable of dealing with different types of transition rate functions. In a statement to Advances in Engineering, Professor Jiawen Hu explained their work offers useful insights for maintenance engineers to calculate the steady-state availability of a system under proposed preventive maintenance policy, in addition to assisting in making an effective preventive maintenance plan to increase the steady-state availability of a system.

About the author

Naichao WANG is a lecturer in the School of Reliability and Systems Engineering of Beihang University, China. He got a bachelor’s degree in solid mechanics in 2000 from Beihang University and he got a Ph.D. degree in aerospace system engineering from Beihang University in 2008, China. His research interests include reliability engineering, inventory optimization, multi-state system modeling, maintenance decision and logistics. He has published over 20 articles on these topics. Articles published in journals such as Reliability Engineering and System Safety, IISE Transactions, Acta Aeronautica et Astronautica Sinica, Journal of Beijing University of Aeronautica and Astronautica, Journal of Systems Engineering and Electronics, etc.

About the author

Jiawen HU is an associate professor in School of Astronautics and Aeronautic, University of Electronic Science and Technology of China, Chengdu, China. He received the B.S. degree in mechanical engineering from Shanghai Jiao Tong University, Shanghai, China, in 2009, the M.S. degree in mechanical engineering from Chinese Academy of Sciences, Beijing, China, in 2012, and the Ph.D. degree in industrial engineering from Shanghai Jiao Tong University, Shanghai, China, in 2017. He was a research fellow with the Department of Industrial Systems Engineering and Management, National University of Singapore from 2017 to 2020. His research interests include maintenance optimization, degradation modeling. His work has appeared in journals including IEEE Transactions on Industrial Informatics, IISE Transactions, IEEE Transactions on Reliability, Reliability Engineering & System Safety, Journal of Manufacturing Systems, International Journal of Production Research.

About the author

Lin MA is an associate professor in Reliability and System Engineering School of Beihang University. He received his BS and Ph.D. in Aircraft Design from Beihang University, China. His research interests include Integrated Logistics Support, System Engineering, Virtual Maintenance and Aircraft Design. He has published over 30 articles on these topics.

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About the author

Haitao LIAO is a Professor, and John and Mary Lib White Endowed Systems Integration Chair in the Department of Industrial Engineering at University of Arkansas C Fayetteville. He received a Ph.D. degree in industrial and systems engineering from Rutgers University in 2004. He also earned M.S. degrees in industrial engineering and statistics from Rutgers University, and a B.S. degree in electrical engineering from Beijing Institute of Technology. His research interests include: (i) reliability models, (ii) maintenance and service logistics, (ii) prognostics, (iv) probabilistic risk assessment, and (v) analytics of sensor data. His research has been sponsored by the U.S. National Science Foundation, Department of Energy, Nuclear Regulatory Commission, Oak Ridge National Laboratory, and industry. The research findings of his group have been published in IISE Transactions, European Journal of Operational Research, Naval Research Logistics, IEEE Transactions on Reliability, IEEE Transactions on Cybernetics, The Engineering Economist, Reliability Engineering & System Safety, etc. In 2014, he served as Chair of INFORMS Quality, Statistics and Reliability (QSR) Section, and President of IISE Quality Control and Reliability Engineering (QCRE) Division.

He served as Associate Editor for Journal of Quality Technology and IEEE Transactions on Reliability, and currently serves as Associate Editor for IISE Transactions on Quality and Reliability Engineering. He received the U.S. National Science Foundation CAREER Award in 2010, the IISE QCRE William A.J. Golomski Award for three times, 2013 QCRE Track Best Paper Award, 2015 Stan Ofsthun Best Paper Award, and the prestigious 2017 Alan O. Plait Award for Tutorial Excellence.

Reference

Naichao Wang, Jiawen Hu, Lin Ma, Boping Xiao, Haitao Liao. Availability Analysis and Preventive Maintenance Planning for Systems with General Time Distributions. Reliability Engineering and System Safety: volume 201 (2020) 106993.

Go To Reliability Engineering and System Safety

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