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 …

Process monitoring is crucial for maintaining favorable operating conditions and has
received considerable attention in previous decades. Currently, a plant-wide process …

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 …

Objective: Driver drowsiness detection is a key technology that can prevent fatal car
accidents caused by drowsy driving. The present work proposes a driver drowsiness …

In this paper, we review multivariate control charts designed for monitoring changes in a
covariance matrix that have been developed in the last 15 years. The focus is on control …

The Principal Component Analysis (PCA) and the Partial Least Squares (PLS) are two
commonly used techniques for process monitoring. Both PCA and PLS assume that the data …

In this study, a new approach for time series based condition monitoring and fault diagnosis
based on bidirectional recurrent neural networks is presented. The application of …

For plant-wide process monitoring, most traditional multiblock methods are under the
assumption that some process knowledge should be incorporated for dividing the process …

A novel multivariate and multiscale statistical process monitoring method is proposed with
the aim of detecting incipient failures in large slewing bearings, where subjective influence …

Many of the current multivariate statistical process monitoring techniques (such as principal
component analysis (PCA) or partial least squares (PLS)) do not utilize the non-Gaussian …