A neural-network-based proportional hazard model for IoT signal fusion and failure prediction

Abstract

Accurate prediction of Remaining Useful Life (RUL) plays a critical role in optimizing condition-based maintenance decisions. In this article, a novel joint prognostic modeling framework that simultaneously combines both time-to-event data and multi-sensor degradation signals is proposed. With the increasing use of IoT devices, unprecedented amounts of diverse signals associated with the underlying health condition of in-situ units have become easily accessible. To take full advantage of the modern IoT-enabled engineering systems, we propose a specialized framework for RUL prediction at the level of individual units. Specifically, a Bayesian linear regression model is developed for the multi-sensor degradation signals and a functional neural network is proposed to allow the proportional hazard model to characterize the complex nonlinearity between the hazard function and degradation signals. Based on the proposed model, an online model updating procedure is established to accurately predict RUL in real time. The advantageous features of the proposed method are demonstrated through simulation studies and the application to a high-fidelity gas turbine engine dataset.

Keywords:

• Cox PH model
• degradation data
• joint prognostic model
• neural networks
• remaining useful life prediction

Additional information

Funding

Jianguo Wu was partially supported by National Natural Science Foundation of China grant NSFC-71932006, NSFC-72171003, NSFC-51875003.

Notes on contributors

Yuxin Wen

Yuxin Wen received a BS degree in medical informatics and engineering from Sichuan University, Chengdu, China, in 2011, an MS degree in biomedical engineering from Zhejiang University, Hangzhou, China, in 2014, and a PhD degree in electrical and computer engineering from the University of Texas at El Paso (UTEP), El Paso, TX, USA, in 2020. She is currently an assistant professor in the Dale E. and Sarah Ann Fowler School of Engineering at Chapman University, Orange, CA, USA. Her research interests are focused on statistical modeling, prognostics, and reliability analysis.

Xinxing Guo

Xinxing Guo received a BS degree in engineering mechanics at the Department of Mechanics and Engineering Science, Peking University, Beijing, China, in 2018. He is currently pursuing a PhD degree at the Department of Industrial Engineering and Management, Peking University, Beijing. His research interests include quality control and reliability engineering and machine learning.

Junbo Son

Junbo Son received a BS degree in industrial systems and information engineering from the Korea University, Seoul, South Korea, in 2010, and an MS in statistics and a PhD degree in industrial & systems engineering from the University of Wisconsin-Madison, Madison, WI, USA, in 2015 and 2016, respectively. He is currently an assistant professor in the Alfred Lerner College of Business & Economics at the University of Delaware, Newark, DE, USA. His research interests include data-driven reliability engineering, medical informatics for advanced healthcare systems, and data analytics for solving various operations management problems.

Jianguo Wu

Jianguo Wu received a BS degree in mechanical engineering from Tsinghua University, Beijing, China in 2009, an MS degree in mechanical engineering from Purdue University in 2011, and an MS degree in statistics in 2014 and PhD degree in industrial and systems engineering in 2015, both from the University of Wisconsin-Madison. Currently, he is an assistant professor in the Department of Industrial Engineering and Management at Peking University, Beijing, China. He was an assistant professor at the Department of Industrial, Manufacturing and Systems Engineering at UTEP, TX, USA from 2015 to 2017. His research interests are mainly in quality control and reliability engineering of intelligent manufacturing and complex systems through engineering-informed machine learning and advanced data analytics. He is a recipient of the STARS Award from the University of Texas Systems, Overseas Distinguished Young Scholars from China, P&G Faculty Fellowship, BOSS Award from MSEC, and several Best Paper Award/Finalists from INFORMS/IISE Annual Meetings. He is an associate editor of the Journal of Intelligent Manufacturing, and a member of IEEE, INFORMS, IISE, and SME.