In this article, a challenging fault diagnosis task is studied, in which no samples of the target
faults are available for the model training. This scenario has hardly been studied in industrial …

Multivariate statistical process monitoring involves dimension reduction and latent feature
extraction in large-scale processes and typically incorporates all measured variables …

In recent years, machine learning algorithms have been successfully applied to industrial
processes. However, the concurrent analysis of static and dynamic representations has not …

Conventionally, the transition probabilities in the interacting multiple model (IMM) are often
fixed based on the prior information. However, this conservative setting may result in …

Discriminant analysis, as a popular supervised classification method, has been successfully
used in fault diagnosis, which, however, involves a linear combination of all variables, and …

In this study, a fault‐tolerant structure and its controlling method for a cascaded H‐bridge
multilevel inverter is introduced. When a fault occurs in one of the modules, the proposed …

As an unmanned system, the multirotor has gained widespread attention in practical aviation
engineering, because of its ease of use, reliability and maintainability. From the perspective …

This paper presents new methods for set-valued state estimation of nonlinear discrete-time
systems with unknown-but-bounded uncertainties. A single time step involves propagating …

This paper focuses on the fault-tolerant control problem for an Internet-based three-tank
system in the presence of possible sensor bias faults. The Internet-based three-tank system …

For zero-shot fault diagnosis, we need to use seen faults to diagnose the class of faults that
have never been seen before. Zero-shot learning (ZSL) transfers the knowledge of seen …