TY - JOUR
T1 - A hybrid data- and model-driven learning framework for remaining useful life prognostics
AU - Cao, Hongjie
AU - Xiao, Wei
AU - Sun, Jian
AU - Gan, Ming Gang
AU - Wang, Gang
N1 - Publisher Copyright:
© 2024 Elsevier Ltd
PY - 2024/9
Y1 - 2024/9
N2 - The efficient and safe production of machinery equipment relies on the health of its mechanical components, making prognostics and health management (PHM) a critical aspect of production processes. One key PHM measure is the remaining useful life (RUL), which estimates the expected lifespan of a component in a production line before requiring repair or replacement. However, state-of-the-art RUL prediction methods, including data-driven, model-based, and hybrid approaches, face limitations such as incomplete/imprecise physical models, uncertainties in degradation processes, and measurement data noise. To address these limitations, this paper proposes a novel hybrid RUL prediction framework that combines the strengths of data-based and model-driven approaches. The framework includes an exponential model to leverage physical knowledge and a multi-head attention transformer to extract information from data. An extended Kalman filter is used to estimate unknown degradation process parameters and provide physical model information for the prediction process. A regression token is introduced to efficiently fuse the deep learning model and the stochastic filtering method. Numerical tests using real-world rolling bearing degradation datasets demonstrate the superiority of the proposed method over competitive alternatives.
AB - The efficient and safe production of machinery equipment relies on the health of its mechanical components, making prognostics and health management (PHM) a critical aspect of production processes. One key PHM measure is the remaining useful life (RUL), which estimates the expected lifespan of a component in a production line before requiring repair or replacement. However, state-of-the-art RUL prediction methods, including data-driven, model-based, and hybrid approaches, face limitations such as incomplete/imprecise physical models, uncertainties in degradation processes, and measurement data noise. To address these limitations, this paper proposes a novel hybrid RUL prediction framework that combines the strengths of data-based and model-driven approaches. The framework includes an exponential model to leverage physical knowledge and a multi-head attention transformer to extract information from data. An extended Kalman filter is used to estimate unknown degradation process parameters and provide physical model information for the prediction process. A regression token is introduced to efficiently fuse the deep learning model and the stochastic filtering method. Numerical tests using real-world rolling bearing degradation datasets demonstrate the superiority of the proposed method over competitive alternatives.
KW - Extended Kalman filter
KW - Hybrid method
KW - Multi-head attention mechanism
KW - Remaining useful life prediction
UR - http://www.scopus.com/inward/record.url?scp=85196523356&partnerID=8YFLogxK
U2 - 10.1016/j.engappai.2024.108557
DO - 10.1016/j.engappai.2024.108557
M3 - Article
AN - SCOPUS:85196523356
SN - 0952-1976
VL - 135
JO - Engineering Applications of Artificial Intelligence
JF - Engineering Applications of Artificial Intelligence
M1 - 108557
ER -