Process fault detection and diagnosis (FDD) is a predominant task to ensure product quality
and process reliability in modern industrial systems. Those traditional FDD techniques are …

Fault detection and diagnosis (FDD) systems are developed to characterize normal
variations and detect abnormal changes in a process plant. It is always important for early …

Process monitoring is crucial for maintaining favorable operating conditions and has
received considerable attention in previous decades. Currently, a plant-wide process …

Data-driven methods have been regarded as desirable methods for fault detection and
diagnosis (FDD) of practical chemical processes. However, with the big data era coming …

Both linear and nonlinear relationships may exist among process variables, and monitoring
a process with such complex relationships among variables is imperative. However …

Deep learning is a powerful tool for feature representation, and many methods based on
convolutional neural networks (CNNs) and recurrent neural networks (RNNs) have been …

Kernel methods are a class of learning machines for the fast recognition of nonlinear
patterns in any data set. In this paper, the applications of kernel methods for feature …

Technological advances led to increasingly larger industrial quality-related datasets calling
for process monitoring methods able to handle them. In such context, the application of …

Complex industrial processes may be formulated with hybrid correlations, indicating that
linear and nonlinear relationships simultaneously exist among process variables, which …

In order to ensure the long-term stable operation of a large-scale industrial process, it is
necessary to detect and solve the minor abnormal conditions in time. However, the large …