Building Degradation Index with Variable Selection for Multivariate Sensory Data

TL;DR

This paper introduces a new method for constructing a degradation index from multivariate sensor data, automatically selecting the most relevant signals, and demonstrating improved performance over existing approaches.

Contribution

A novel additive nonlinear model with adaptive group penalty for automatic sensor signal selection in degradation index construction.

Findings

Outperforms existing methods in simulations

Effective sensor signal selection for degradation modeling

Validated on NASA jet engine sensor data

Abstract

The modeling and analysis of degradation data have been an active research area in reliability and system health management. As the senor technology advances, multivariate sensory data are commonly collected for the underlying degradation process. However, most existing research on degradation modeling requires a univariate degradation index to be provided. Thus, constructing a degradation index for multivariate sensory data is a fundamental step in degradation modeling. In this paper, we propose a novel degradation index building method for multivariate sensory data. Based on an additive nonlinear model with variable selection, the proposed method can automatically select the most informative sensor signals to be used in the degradation index. The penalized likelihood method with adaptive group penalty is developed for parameter estimation. We demonstrate that the proposed method outperforms existing methods via both simulation studies and analyses of the NASA jet engine sensor data.