With the wide adoption of building automation system, and the advancement of data,
sensing, and machine learning techniques, data-driven fault detection and diagnostics …

This review article examines recent advances in the use of machine learning for process
industries. The article presents common process industry tasks that researchers are solving …

Long short-term memory (LSTM) has been widely applied to real-time automated natural
gas leak detection and localization. However, LSTM approach could not provide the …

When deep learning-based models are employed for the fault diagnosis of chemical
processes, problems of poor calculation accuracy and efficiency often occur in the scenarios …

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 …

Data-driven intelligent technologies are promoting a disruptive digital transformation of
human society. Industrial accident prevention is also amid this change. Although many …

As the digitalization of process industry deepens, process fault detection and diagnosis
(FDD) is an essential tool to ensure safe production in chemical industries. However, FDD …

Risks in chemical plants can generally be divided into Black Swan incidents and Gray Rhino
incidents. Black Swan events are unexpected and have a significant impact. Frequently, a …

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 …

A well-known shortcoming of the traditional canonical correlation analysis (CCA) is the lack
of robustness against outliers. This shortcoming hinders the application of CCA in the case …