Bayesian Network Approach for Studying the Operational Reliability and Remaining Useful Life

Authors

  • Debasis Jana Indian Institute of Technology (BHU), Varanasi-221005, India
  • Deepak Kumar Indian Institute of Technology (BHU), Varanasi-221005, India
  • Suprakash Gupta Indian Institute of Technology (BHU), Varanasi-221005, India
  • Sukomal Pal Indian Institute of Technology (BHU), Varanasi-221005, India
  • Sandip Ghosh Indian Institute of Technology (BHU), Varanasi-221005, India

DOI:

https://doi.org/10.13052/jrss0974-8024.16210

Keywords:

Operational reliability, Bayesian network, Remaining useful life

Abstract

Reliability study plays a significant role in supporting the operation of any machinery working in a dynamic and harsh environment such as mining, and construction industries. This quality is inherently uncertain and a stochastic variable of any system. This study focused on the effects of operating conditions (OCs) on the operational reliability and remaining useful life (RUL) of machinery. A probabilistic graphical method called Bayesian Network (BN) was used to study the effect of OCs on the system performance. The developed methodology has been demonstrated by analyzing the operational reliability and predicting the RUL of electrical motors operated in heavy mining machinery. The failure probabilities estimated from the historical data of the motor system are failure likelihood, and OCs are the evidence in the developed BN model. It has been observed that the performance and RUL of the motor are significantly influenced by OCs and maintenance. A threshold value of reliability at which the motor system requires maintenance or replacement has been proposed to guide management in decision-making. This study will be beneficial for designing an appropriate maintenance schedule, reducing unplanned production downtime, and reducing the maintenance cost of electrical motors operated particularly in dynamic and harsh environmental industries.

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Author Biographies

Debasis Jana, Indian Institute of Technology (BHU), Varanasi-221005, India

Debasis Jana is a senior researcher at the Computer and Data Analytics Lab, Department of Mining Engineering, Indian Institute of Technology (Banaras Hindu University), Varanasi. He earned his Bachelor of Engineering, and Master of Technology in 2011, 2014 respectively. He has so far authored 1 book chapter, about 7 articles in reputed journals and about 10 research papers in conferences or workshops. His research interests include reliability engineering, data analytics, and related areas of statistics.

Deepak Kumar, Indian Institute of Technology (BHU), Varanasi-221005, India

Deepak Kumar is currently working in Orbit & Skyline semiconductors as the Reliability Engineer. He has earned his PhD from the Indian Institute of Technology (Banaras Hindu University) Varanasi, India. His PhD title was “Reliability Analysis of Dragline Using Bayesian Network”. He has published eight research papers and seven conference papers in reputed international; and national publications. Apart from this, he has attained more than dozens of workshops and FDPs. He is also interested in investigating reliability analysis using repairable and non-repairable concept on component and system level, application of different maintenance schedules and FMECA. Also, he shows an interest in remaining useful life estimation and uses of machine learning techniques like different modes of regression, classifiers, neural networks in reliability estimation, and uses of risk and safety aspects in mining industries. He holds the MTech in Design and Production Engineering from the National Institute of Technology Durgapur, India. He pursued his Bachelor of Engineering from Nagpur University, Nagpur.

Suprakash Gupta, Indian Institute of Technology (BHU), Varanasi-221005, India

Suprakash Gupta is a Professor and Head of the Department of Mining Engineering at the Indian Institute of Technology (Banaras Hindu University), Varanasi. Prof. Gupta completed his B.E. in Mining Engineering from the University of Calcutta (Bengal Engineering College, Shibpure, Howrah, WB) in 1992 securing First class. He did his M.Tech. and Ph.D. in Mining Engineering from Indian Institute of Technology, Kharagpur with First class in his Masters Course. He has been working in the field of in-service reliability estimation and maintenance of equipment and systems for over 25 years. Prof. Gupta has published more than 100 research publications in various international/national Journals and Sem. / Conf. / Symp. till date.

Sukomal Pal, Indian Institute of Technology (BHU), Varanasi-221005, India

Sukomal Pal is an associate professor in the Department of Computer Science and Engineering at IIT(BHU), India. He earned his Bachelor of Engineering, Master of Technology and Doctor of Philosophy in 1999, 2005 and 2012 respectively. He has been serving as the associate editor of Springer Nature Computer Science journal since 2021. He has so far authored 2 books including an undergraduate textbook, 1 book chapter, about 30 articles in reputable journals and about 50 research papers in conferences or workshops. His research interests include information retrieval, recommender system, data analytics and related areas of text processing.

Sandip Ghosh, Indian Institute of Technology (BHU), Varanasi-221005, India

Sandip Ghosh received Bachelor of Engineering degree in electrical engineering from the Bengal Engineering College (DU), Shibpur, India, in 1999; master’s degree in control system engineering from Jadavpur University, India, in 2003, and Ph.D. degree from IIT Kharagpur, Kharagpur, India, in 2010. He was with the National Institute of Technology, Rourkela, India, and was also a Postdoctoral Fellow with the University of Cape Town, South Africa. He is currently an Associate Professor in electrical engineering with the IIT (BHU), Varanasi, India. His research interests include Robust control design, decentralized control, and networked control systems.

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Published

2024-05-14

How to Cite

Jana, D. ., Kumar, D. ., Gupta, S. ., Pal, S., & Ghosh, S. (2024). Bayesian Network Approach for Studying the Operational Reliability and Remaining Useful Life. Journal of Reliability and Statistical Studies, 16(02), 373–392. https://doi.org/10.13052/jrss0974-8024.16210

Issue

2023: Vol 16 Iss 2

Section

Articles