Although cerebral stroke is a important public worldwide health problem with more than 43
million global cases reported recently, more than 90% of metabolic risk factors are …

Large-scale processes have become common, and fault detection for such processes is
imperative. This work studies the data-driven distributed local fault detection problem for …

In this article, a methodology based on machine learning for fault detection in continuous
processes is presented. It aims to monitor fully distributed scenarios, such as the Tennessee …

It is hard to obtain sufficient fault samples in most real-world industrial scenarios. This has
raised the need of addressing the critical issue of imbalanced fault diagnosis that remains a …

Traditional process monitoring methods take all the measured variables into account,
whereas it will be inappropriate for indicating quality-relevant faults. Some measured …

In this paper, a Bayesian Network-K2 Algorithm-Expectation Maximization (BN-K2-EM)
approach is proposed to quantify the intensity of coupling influence among the operational …

This paper presents an approach to fault diagnosis with online detection of novel faults and
automatic learning using fuzzy clustering techniques. In the off-line learning stage, the …

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

Circulating water pumps (CWPs), essential to the cooling systems of nuclear power units
(NPUs), are crucial for maintaining the safety and reliability of nuclear power plants …

Identification of abnormal conditions is essential in the chemical process. With the rapid
development of artificial intelligence technology, deep learning has attracted a lot of …