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 …

It is well-documented how artificial intelligence can have (and already is having) a big
impact on chemical engineering. But classical machine learning approaches may be weak …

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 …

Data-based process monitoring has become a key technology in process industries for
safety, quality, and operation efficiency enhancement. This paper provides a timely update …

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 …

Accident prevention is one of the most desired and challenging goals in process industries.
For accident prevention, fault detection and diagnosis (FDD) is critical. FDD has been an …

This paper presents a comprehensive review of the historical development, the current state
of the art, and prospects of data-driven approaches for industrial process monitoring. The …

A kernel independent component analysis (KICA) is widely regarded as an effective
approach for nonlinear and non-Gaussian process monitoring. However, the KICA-based …

Process monitoring can be considered as a one-class classification problem, the aim of
which is to differentiate the normal data samples from the faulty ones. This paper introduces …

To achieve reliable and automatic anomaly detection (AD) for large equipment such as
liquid rocket engine (LRE), multisource data are commonly manipulated in deep learning …