In recent years, deep learning has achieved significant success in various fields, including natural language processing, autonomous driving, and computer vision. In the realm of prognostics and health management (PHM) for rolling bearings in rotating machinery—such as aero engines, wind turbines, and high-speed trains—numerous intelligent PHM methodologies have emerged to provide accurate and adaptable machinery fault diagnostics and prognostics. However, methodologically speaking, there is no one-size-fits-all approach. It is widely acknowledged that these data-driven approaches still possess considerable limitations, hindering their widespread adoption in industrial settings.

Three primary challenges persist: (1) the lack of interpretability in deep learning methods, particularly in machinery fault diagnosis, where diagnostic models must be transparent to foster trust in the results and inform maintenance decisions; (2) the limited generalizability and reliability of bearing remaining useful life (RUL) prediction models. When training data is scarce, even under identical operating conditions and with the same bearing types, current RUL models demonstrate suboptimal accuracy. In addition, ensuring the reliability of RUL predictions is an important consideration for making informed maintenance decisions in real-world scenarios; and (3) the difficulty in deploying intelligent diagnosis models to edge devices, which hinders their integration into real-world industrial settings.

Therefore, this dissertation aims to address these challenges by constructing the paradigm of integrating traditional signal processing and modern deep learning methods. We formally define this approach as signal processing-empowered neural networks, which synthesize the complementary strengths of both domains. This framework provides three key advantages: (1) integrating rigorous signal processing theory to improve model interpretability; (2) leveraging the robust feature representation capabilities of signal processing techniques to enhance deep learning model generalizability and auxiliary exponential model to quantify the reliability of RUL predictions; and (3) enabling faster computation and greater accuracy, thereby facilitating the edge device deployment of lightweight models. The research contents are summarized as follows:

We are pleased to welcome Ruohan Li, who recently joined our group as a research assistant. Ruohan Li holds a Bachelor’s degree in Economics and Finance from the University of Toronto and a Master’s degree in Business Analytics from Ivey Business School at Western University.

The group for risk, reliability, and resilience informatics warmly welcomes the following PhD students to join the team:

Tao Wang, and Xinru Zhang.