A hybrid prognostic & health management framework across multi-level engineering systems with scalable convolution neural networks and adjustable functional regression models

Abstract

With the rapid development of sensor technology and mechatronics integration, there are emerging demands for achieving prognostic and health management (PHM) within a general framework across multi-level engineering systems. However, current PHM frameworks are mainly supported either by field data or degradation models, which are always limited in inadequate robustness to systematically increase operational availability and reduce maintenance costs. For extending the scalability and flexibility of PHM services, this paper proposes a novel hybrid framework that integrates the scalable convolution neural network (SCNN) with the adjustable functional regression model (AFRM) through a modified linear filter. This proposed framework first establishes a data-driven SCNN with scalable time sequences and kernel sizes to enhance the remaining useful life (RUL) generalization. Then, enabled by an adjustable semi-parametric degradation process model, AFRM with flexible lifetime distributions is designed to evaluate time-to-failure (TTF) in real time. Finally, a modified Kalman filter has been explored to coordinate data-driven RUL results with model-based TTF values to schedule predictive maintenance (PdM). High-accuracy prediction, multi-level systems, high-flexibility evaluation are thus encompassed within the hybrid progressive modification. The proposed framework has been practically validated through multi-level studies including the component-level bearings, subsystem-level engines, and system-level machines. Results demonstrate that our hybrid PHM framework achieves significant improvements in prediction accuracy, evaluation adaptability, and scheduling effectiveness.