Kernel methods and neural networks (NNs) are two mainstream nonlinear data modeling
methods and have been widely applied to industrial process monitoring. However, they both …

Load fluctuations, unexpected disturbances, and switching of operating states typically make
actual industrial processes exhibit nonstationary. In nonstationary processes, the statistical …

Actual industrial processes often show nonstationary characteristics, so nonstationary
process monitoring is significant to ensure the safety and reliability of industrial processes …

The electro-pneumatic brake system (EPBS) is an essential part of electric multiple units.
Fault diagnosis (FD) methods play an important role in the safe operation of EPBS, and have …

Faulty variable isolation and amplitude estimation are of great importance to support the
decision-making for system maintenance but lack sufficient studies, especially concerning …

Deep neural networks have evolved into a powerful tool applicable for a wide range of
problems. However, a clear understanding of their internal mechanism has not been …

Abstract Machine learning models, typically deep learning models, often come at the cost of
explainability. To generate explanations of such systems, models need to be rooted in first …

Stationary subspace analysis (SSA) is an emerging algorithm for nonstationary process
monitoring, which establishes a linear relationship between nonstationary variables and …

Fault diagnosis and prognosis have attracted huge attention in industry and academia for
the increasing requirements on reliability, availability, maintainability, and safety. Despite the …