Safe and stable operation plays an important role in the chemical industry. Fault detection
and diagnosis (FDD) make it possible to identify abnormal process deviations early and …

The failure of a rolling bearing may cause the shutdown of mechanical equipment and even
induce catastrophic accidents, resulting in tremendous economic losses and a severely …

Data mining and analytics have played an important role in knowledge discovery and
decision making/supports in the process industry over the past several decades. As a …

As a key component in electromechanical systems, the health condition monitoring of rolling
bearings is crucial for the safe operation of the whole system. For this purpose, the …

The primary task of rotating machinery fault diagnosis is to extract more fault feature
information from the measured signals, so that its diagnostic result is more accurate and …

Deep learning-based soft sensor has been widely used for quality prediction in modern
industry. Traditional deep learning like stacked autoencoder (SAE) only captures the feature …

In this paper, a novel data projection method, local and global principal component analysis
(LGPCA) is proposed for process monitoring. LGPCA is a linear dimensionality reduction …

Currently, it is a great challenge to effectively acquire more widespread equipment health
information for guaranteeing safe production and timely fault maintenance in the process of …

Traditional kernel principal component analysis (KPCA) concentrates on the global structure
analysis of data sets but omits the local information which is also important for process …

Complex industrial processes are commonly characterized with multiple operation modes.
The existing manifold learning-based process monitoring methods describe each mode …