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 …

Principal component analysis (PCA) is often applied for analyzing data in the most diverse
areas. This work reports, in an accessible and integrated manner, several theoretical and …

Abstract Wireless Sensor Network (WSN) being highly diversified cyber–physical system
makes it vulnerable to numerous failures, which can cause devastation towards safety …

This paper deals with the problem of fault detection and diagnosis in sensors considering
erratic, drift, hard-over, spike, and stuck faults. The data set containing samples of the above …

Although the partial least squares approach is an effective fault detection method, some
issues of nonlinear process monitoring related to key performance indicators (KPIs) still …

Early and accurate fault detection and diagnosis for modern manufacturing processes can
minimise downtime, increase the safety of plant operations, and reduce costs. Such process …

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 …

This paper provides an overview and analysis of statistical process monitoring methods for
fault detection, identification and reconstruction. Several fault detection indices in the …

In this paper, a new nonlinear process monitoring technique based on kernel principal
component analysis (KPCA) is developed. KPCA has emerged in recent years as a …

Multiscale principal‐component analysis (MSPCA) combines the ability of PCA to
decorrelate the variables by extracting a linear relationship with that of wavelet analysis to …