Most statistical analysis technologies use detection thresholds for fault diagnosis, which
often cannot effectively characterize some specific faults in a statistical manner. However …

Data-based process monitoring is gaining increasing attention, especially deep learning
modeling methods. Given that process data are inherently dynamic, the dynamic …

Fault or anomaly detection is one of the key problems faced by the chemical process
industry for achieving safe and reliable operation. In this study, a novel methodology …

As representation scheme can severely limit the window by which the system observes its
world, deep learning for fault diagnosis is put forward in this paper. It is a real time online …

Deep neural networks (DNNs) are popular in process monitoring for its remarkable feature
extraction from data. However, the increased dimension and correlation of the process …

This study aims to enhance the accuracy and interpretability of fault diagnosis. To address
this objective, we present a novel attention-based CNN method that leverages image-like …

Industrial applications of fault detection and diagnosis face great challenges as they require
not only accurate identification of faulty statuses but also the effective understandability of …

Fault detection and diagnosis is one of the most critical components of preventing accidents
and ensuring the system safety of industrial processes. In this paper, we propose an …

This paper proposes a hierarchical sparse artificial neural network for classifying the faults in
dynamic processes base on limited labeled data. The Stacked auto-encoders (SAE) is …

In large-scale industrial processes, fault diagnosis is of paramount importance, as faults
jeopardize the stability and performance of processes. However, effective fault diagnosis …