In this era of Industry 4.0, there are continuing efforts to develop fault detection and
diagnosis methods that are fully autonomous; these methods are self-learning, with little or …

For fault detection and diagnosis in large‐scale industrial systems, traditional methods have
a low classification accuracy, which is an issue. This paper proposes a fault diagnosis …

In this article, we present (1) a feature selection algorithm based on nonlinear support vector
machine (SVM) for fault detection and diagnosis in continuous processes and (2) results for …

A novel networked process monitoring, fault propagation identification, and root cause
diagnosis approach is developed in this study. First, process network structure is determined …

In the field of fault detection, the nonstationary characteristics caused by external
disturbances of wind turbine (WT) and other reasons can mask the fault signals, while the …

Unlike many other techniques used in process control, which are widely applied in practice
and play significant roles, abnormal situation management (ASM) still relies heavily on …

As a method widely used in fault detection, principal component analysis (PCA) still has
challenges in applicability due to its sensitivity to outliers and its difficulty in principal …

Given their potentially enormous risk, process monitoring and fault diagnosis for chemical
plants have recently been the focus of many studies. Based on hazard and operability …

Highlights•Smart Manufacturing requires the elimination of safety incidents when maximize
economic competitiveness.•Methods of abnormal situation management with safety risk …

Fault detection and diagnosis for industrial systems has been an important field of research
during the past years. Among these systems, the Tennessee Eastman process is extensively …