A Shape-Constrained Neural Data Fusion Network for Health Index Construction and Residual Life Prediction

• Published in: IEEE transaction on neural networks and learning systems Vol. 32; no. 11; pp. 5022 - 5033
• Main Authors: Li, Zhen, Wu, Jianguo, Yue, Xiaowei
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.11.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive algorithms; Algorithms; Condition monitoring; Construction; Data integration; Degradation; Failure times; Forecasting; health index; Health Status Indicators; Humans; Life prediction; Longevity; Modular systems; neural data fusion network; Neural networks; Neural Networks, Computer; Parameter estimation; Predictions; Propulsion systems; Remaining life assessment; remaining useful life (RUL) prediction; shape constrained
• ISSN: 2162-237X; 2162-2388; 2162-2388
• DOI: 10.1109/TNNLS.2020.3026644

With the rapid development of sensor technologies, multisensor signals are now readily available for health condition monitoring and remaining useful life (RUL) prediction. To fully utilize these signals for a better health condition assessment and RUL prediction, health indices are often constructed through various data fusion techniques. Nevertheless, most of the existing methods fuse signals linearly, which may not be sufficient to characterize the health status for RUL prediction. To address this issue and improve the predictability, this article proposes a novel nonlinear data fusion approach, namely, a shape-constrained neural data fusion network for health index construction. Especially, a neural network-based structure is employed, and a novel loss function is formulated by simultaneously considering the monotonicity and curvature of the constructed health index and its variability at the failure time. A tailored adaptive moment estimation algorithm (Adam) is proposed for model parameter estimation. The effectiveness of the proposed method is demonstrated and compared through a case study using the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) data set.