Name
Abstract | ||
|---|---|---|
The modeling and analysis of degradation data have been an active research area in reliability engineering for reliability assessment and system health management. As the sensor 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 with censoring. Based on an additive nonlinear model with variable selection, the proposed method can handle censored data, and can automatically select the 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. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.ress.2022.108704 | Reliability Engineering & System Safety |
Keywords | DocType | Volume |
Adaptive LASSO,General path model,Prognostics,Sensor selection,Splines,System health monitoring | Journal | 227 |
ISSN | Citations | PageRank |
0951-8320 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yueyao Wang | 1 | 0 | 0.34 |
| I-Chen Lee | 2 | 0 | 0.34 |
| Yili Hong | 3 | 290 | 28.48 |
| Xinwei Deng | 4 | 3 | 2.41 |