Remaining useful life prediction using the similarity-based integrations of multi-sensors data

Abstract

AbstractIn prognostics and health management, the system’s degradation condition assessment and corresponding remaining useful life prediction are the most important tasks. Both of these processes are heavily dependent on information gathered by multiple sensors, which eventually causes data fusion-related complex problems. Typically, sensor information contains the speed, pressure, temperature, and similar other types of various system data. These systems’ data obtained through sensors can be utilized as a part of the evidence in the evidence-based estimation method. In this work, an artificial intelligence-based novel framework for estimating the remaining useful life using data fusion has been presented. The Dempster–Shafer extended theory is adopted for sensor information modeling and data fusion. Besides, two different scenarios are introduced to determine the similarity between the studied system and the available evidence. As a case study, the turbofan dataset is demonstrated to assess the proposed method. Based on the results, our integrated proposed method performs very competitively compared with the existing methods based on standard scores and performance criteria.Keywords: Artificial neural networksDempster–Shafer theoryinformation integrationK-means clusteringprognostics & health managementremaining useful lifesensor data Additional informationNotes on contributorsMohammad BaharshahiMohammad Baharshahi is a PhD candidate in the Department of Industrial Engineering at Iran University of Science and Technology. His research interest is condition based maintenence, reliability centered maintenance and data mining.S. Mohammad SeyedhosseiniS. Mohammad Seyedhosseini is a professor in Department of Industrial Engineering at Iran University of Science and Technology. His focuses on maintenance planning, production management and supply chain management.Shah M LimonShah M Limon is an Assistant Professor of Industrial & System Engineering at Slippery Rock University of Pennsylvania, USA. He received his M.Sc. and Ph.D. from the Department of Industrial and Manufacturing Engineering at North Dakota State University, Fargo, USA, and B.Sc. degree in Industrial and Production Engineering from Bangladesh University of Engineering & Technology, Dhaka, Bangladesh. His research interest includes but is not limited to reliability-based design, accelerated product testing, stochastic modeling, prognostics with machine learning, network reliability optimization, additive manufacturing, and lean process improvement. Shah’s research work has been published in Quality Technology and Quantitative Management, Quality & Reliability Engineering International, Quality Engineering, Journal of Risk & Reliability, International Journal of Quality & Reliability Management, and International Journal of Quality Engineering and Technology. He is a member of IISE.