Recently, to ensure the reliability and safety of modern large-scale industrial processes, data-
driven methods have been receiving considerably increasing attention, particularly for the …

This paper provides a state-of-the-art review of the methods and applications of data-driven
fault detection and diagnosis that have been developed over the last two decades. The …

This paper presents a data-driven methodology for fault detection and diagnosis (FDD) by
integrating the principal component analysis (PCA) with the Bayesian network (BN). Though …

Aiming at the problem that the most existing fault diagnosis methods could not effectively
recognize the early faults in the rotating machinery, the empirical mode decomposition, fuzzy …

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 …

This paper provides a comparison study on the basic data-driven methods for process
monitoring and fault diagnosis (PM–FD). Based on the review of these methods and their …

Data-driven approaches have gained increasing interests in the fault detection of wind
turbines (WTs) due to the difficulty in system modeling and the availability of sensor data …

Process fault detection and diagnosis (FDD) is a predominant task to ensure product quality
and process reliability in modern industrial systems. Those traditional FDD techniques are …

The aim of this paper is to describe the state-of-the art computer-based techniques for data
analysis to improve operation of wastewater treatment plants. A comprehensive review of …

Partial least squares or projection to latent structures (PLS) has been used in multivariate
statistical process monitoring similar to principal component analysis. Standard PLS often …