TY - JOUR
T1 - Health index-based remaining useful life prediction using functional principal component analysis with multivariate sensor data
AU - Nian, Sirui
AU - Kang, Wenda
AU - Wen, Yingqian
AU - Du, Zibing
AU - Wang, Dianpeng
N1 - Publisher Copyright:
© 2025 Elsevier Ltd.
PY - 2026/4
Y1 - 2026/4
N2 - Accurate prediction of remaining useful life (RUL) is crucial for effective prognostics of industrial devices and health management. With the development of sensor technology, multiple sensor data points can be collected during the device monitoring process. To fully exploit the information from different sensors, various methods have been proposed in the literature to construct a health index (HI) by combining multivariate sensor data to predict RUL. However, most of these methods assume that the sensors are independent. In reality, there are correlations between sensor signals due to shared degradation processes, common environmental factors, or sensor interactions. Ignoring these correlations when constructing the HI can lead to inaccurate RUL predictions. To address this issue, this paper proposes a novel approach that utilizes functional principal component analysis (FPCA) to integrate multivariate sensor data while considering sensor correlations. Compared with other data fusion methods, FPCA can extract orthogonal principal component functions from multiple sensors, effectively avoiding multiple correlations between sensor signals. Furthermore, to correct the systematic bias caused by traditional models that primarily capture global trends, we introduce a residual correction module based on random forest (RF) to refine the RUL prediction. Simulation studies and two real-world case studies demonstrate the superiority of the proposed approach in terms of both accuracy and robustness.
AB - Accurate prediction of remaining useful life (RUL) is crucial for effective prognostics of industrial devices and health management. With the development of sensor technology, multiple sensor data points can be collected during the device monitoring process. To fully exploit the information from different sensors, various methods have been proposed in the literature to construct a health index (HI) by combining multivariate sensor data to predict RUL. However, most of these methods assume that the sensors are independent. In reality, there are correlations between sensor signals due to shared degradation processes, common environmental factors, or sensor interactions. Ignoring these correlations when constructing the HI can lead to inaccurate RUL predictions. To address this issue, this paper proposes a novel approach that utilizes functional principal component analysis (FPCA) to integrate multivariate sensor data while considering sensor correlations. Compared with other data fusion methods, FPCA can extract orthogonal principal component functions from multiple sensors, effectively avoiding multiple correlations between sensor signals. Furthermore, to correct the systematic bias caused by traditional models that primarily capture global trends, we introduce a residual correction module based on random forest (RF) to refine the RUL prediction. Simulation studies and two real-world case studies demonstrate the superiority of the proposed approach in terms of both accuracy and robustness.
KW - Correlation
KW - Functional principal component analysis
KW - Health index
KW - Multivariate sensor data
KW - Remaining useful life
KW - Residual learning
UR - https://www.scopus.com/pages/publications/105024346647
U2 - 10.1016/j.ress.2025.111985
DO - 10.1016/j.ress.2025.111985
M3 - Article
AN - SCOPUS:105024346647
SN - 0951-8320
VL - 268
JO - Reliability Engineering and System Safety
JF - Reliability Engineering and System Safety
M1 - 111985
ER -