TY - JOUR
T1 - Multi-sensor prognostics modeling for applications with highly incomplete signals
AU - Fang, Xiaolei
AU - Yan, Hao
AU - Gebraeel, Nagi
AU - Paynabar, Kamran
N1 - Funding Information:
The authors thank the Editor, AE and referees for their valuable comments. The research of Paynabar is supported by the NSF grants CMMI-1839591.
Publisher Copyright:
© Copyright © 2020 “IISE”.
PY - 2021
Y1 - 2021
N2 - Multi-stream degradation signals have been widely used to predict the residual useful lifetime of partially degraded systems. To achieve this goal, most of the existing prognostics models assume that degradation signals are complete, i.e., they are observed continuously and frequently at regular time grids. In reality, however, degradation signals are often (highly) incomplete, i.e., containing missing and corrupt observations. Such signal incompleteness poses a significant challenge for the parameter estimation of prognostics models. To address this challenge, this article proposes a prognostics methodology that is capable of using highly incomplete multi-stream degradation signals to predict the residual useful lifetime of partially degraded systems. The method first employs multivariate functional principal components analysis to fuse multi-stream signals. Next, the fused features are regressed against time-to-failure using (log)-location-scale regression. To estimate the fused features using incomplete multi-stream degradation signals, we develop two computationally efficient algorithms: subspace detection and signal recovery. The performance of the proposed prognostics methodology is evaluated using simulated datasets and a degradation dataset of aircraft turbofan engines from the NASA repository.
AB - Multi-stream degradation signals have been widely used to predict the residual useful lifetime of partially degraded systems. To achieve this goal, most of the existing prognostics models assume that degradation signals are complete, i.e., they are observed continuously and frequently at regular time grids. In reality, however, degradation signals are often (highly) incomplete, i.e., containing missing and corrupt observations. Such signal incompleteness poses a significant challenge for the parameter estimation of prognostics models. To address this challenge, this article proposes a prognostics methodology that is capable of using highly incomplete multi-stream degradation signals to predict the residual useful lifetime of partially degraded systems. The method first employs multivariate functional principal components analysis to fuse multi-stream signals. Next, the fused features are regressed against time-to-failure using (log)-location-scale regression. To estimate the fused features using incomplete multi-stream degradation signals, we develop two computationally efficient algorithms: subspace detection and signal recovery. The performance of the proposed prognostics methodology is evaluated using simulated datasets and a degradation dataset of aircraft turbofan engines from the NASA repository.
KW - RUL
KW - degradation modelling
KW - missing data
KW - multi-stream signal fusion
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U2 - 10.1080/24725854.2020.1789779
DO - 10.1080/24725854.2020.1789779
M3 - Article
AN - SCOPUS:85089457348
VL - 53
SP - 597
EP - 613
JO - IIE Transactions (Institute of Industrial Engineers)
JF - IIE Transactions (Institute of Industrial Engineers)
SN - 2472-5854
IS - 5
ER -