A generic framework for multisensor degradation modeling based on supervised classification and failure surface

Abstract

In condition monitoring, multiple sensors are widely used to simultaneously collect measurements from the same unit to estimate the degradation status and predict the remaining useful life. In this article, we propose a generic framework for multisensor degradation modeling, which can be viewed as an extension of the degradation models from one-dimensional space to multi-dimensional space. Specifically, we model each sensor signal based on random-effect models and characterize failure events by a multi-dimensional failure surface, which is an extension of the conventional definition of the failure threshold for a single sensor signal. To overcome the challenges in estimating the failure surface, we transform the degradation modeling problem into a supervised classification problem, where a variety of classifiers can be incorporated to estimate the degradation status of the unit based on the underlying signal paths, i.e., the collected sensor signals after removing the noise. As a result, the proposed method gains great flexibility. It can also be used for sensor selection, can handle asynchronous sensor signals, and is easy to implement in practice. Simulation studies and a case study on the degradation of aircraft engines are conducted to evaluate the performance of the proposed framework in parameter estimation and prognosis.

Keywords:

• Asynchronous sensor signals
• data fusion
• sensor selection

Additional information

Funding

This work was supported in part by the office of Naval Research under Grant N00014-17-1-2261, in part by the Department of Energy under award number DE-NE0008805, and in part by the National Science Foundation of China under award numbers 71771004, 71571003, and 71690232.

Notes on contributors

Changyue Song

Changyue Song received a B.S. degree in industrial engineering from Tsinghua University in 2012 and an M.S. degree in industrial engineering from Peking University in 2015. Currently he is a Ph.D. student at the Department of Industrial and Systems Engineering, University of Wisconsin-Madison. His research interests are focused on statistical modeling and improvement of complex systems.

Kaibo Liu

Kaibo Liu received a B.S. degree in industrial engineering and engineering management from the Hong Kong University of Science and Technology in 2009, and an M.S. degree in statistics and Ph.D. degree in industrial engineering from the Georgia Institute of Technology in 2011 and 2013, respectively. Currently, he is an assistant professor at the Department of Industrial and Systems Engineering, University of Wisconsin-Madison. His research interests are focused on data fusion for process modeling, monitoring, diagnosis and prognostics and decision making. Dr. Liu is a member of IEEE, ASQ, INFORMS and IISE.

Xi Zhang

Xi Zhang received a B.S. degree in mechanical engineering and automation from Shanghai Jiaotong University, Shanghai, China, in 2006, and a Ph.D. degree in industrial engineering from the University of South Florida, Tampa, 2010. He is an associate professor with the Department of Industrial Engineering and Management, Peking University, Beijing. His research interests are physical-statistical modeling and analysis for process monitoring, diagnosis and optimization in complex dynamic systems. Dr. Zhang is a member of IEEE, INFORMS, IISE and ASQ.