Unknown correlations (UCs) generally exist in a wide spectrum of practical multi-source
information fusion problems, and thereby, their corresponding fusion problems have …

This review paper is to give a full picture of fault detection and diagnosis (FDD) in complex
systems from the perspective of data processing. As a matter of fact, an FDD system is a data …

In this final part, we discuss fault diagnosis methods that are based on historic process
knowledge. We also compare and evaluate the various methodologies reviewed in this …

Sensors provide insights into the industrial processes, while misleading sensor outputs may
result in inappropriate decisions or even catastrophic accidents. In this article, the Bayesian …

Historical data collected from processes are readily available. This paper looks at recent
advances in the use of data-driven models built from such historical data for monitoring, fault …

Multivariate statistical approaches to fault detection based on historical operating data have
been found to be useful with processes having a large number of measured variables and …

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 …

A new data-based method for nonlinear process modeling is developed in this paper. In the
proposed method, both distance measure and angle measure are used to evaluate the …

Conventional process monitoring based on principal component analysis (PCA) has been
applied to many industrial chemical processes. However, such PCA-based approaches …

Process monitoring is a critical activity in manufacturing industries. A wide variety of data-
driven approaches have been developed and employed for fault detection and fault …