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 …

Soft sensors for regression applications (SSR) are inferential models that use online
available sensors (eg temperature, pressure, flow rate, etc.) to predict quality variables …

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 …

In the last two decades Soft Sensors established themselves as a valuable alternative to the
traditional means for the acquisition of critical process variables, process monitoring and …

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

With ever-accelerating advancement of information, communication, sensing and
characterization technologies, such as industrial Internet of Things (IoT) and high-throughput …

In this article, we review and discuss algorithms for adaptive data-driven soft sensing. In
order to be able to provide a comprehensive overview of the adaptation techniques …

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 …

Partial least squares (PLS) regression is effectively used in process modeling and
monitoring to deal with a large number of variables with collinearity. In this paper, several …

In many industrial plants, development and implementation of advanced monitoring and
control techniques require real-time measurement of process quality variables. However, on …