Statistical degradation modeling and prognostics of multiple sensor signals via data fusion: A composite health index approach

ABSTRACT

Nowadays multiple sensors are widely used to simultaneously monitor the degradation status of a unit. Because those sensor signals are often correlated and measure different characteristics of the same unit, effective fusion of such a diverse “gene pool” is an important step to better understanding the degradation process and producing a more accurate prediction of the remaining useful life. To address this issue, this article proposes a novel data fusion method that constructs a composite Health Index (HI) via the combination of multiple sensor signals for better characterizing the degradation process. In particular, we formulate the problem as indirect supervised learning and leverage the quantile regression to derive the optimal fusion coefficient. In this way, the prognostic performance of the proposed method is guaranteed. To the best of our knowledge, this is the first article that provides the theoretical analysis of the data fusion method for degradation modeling and prognostics. Simulation studies are conducted to evaluate the proposed method in different scenarios. A case study on the degradation of aircraft engines is also performed, which shows the superior performance of our method over existing HI-based methods.

KEYWORDS:

• Indirect supervised learning
• multisensor fusion
• quantile regression

Additional information

Funding

This work was supported in part by the Office of Naval Research under grant N00014-17-1-2261.

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 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. He is a member of IEEE, ASQ, INFORMS, and IISE.