Principal component analysis is one of the most important and powerful methods in
chemometrics as well as in a wealth of other areas. This paper provides a description of how …

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 …

The turnout switch machine is the critical equipment of the signal system, which has a
significant influence on the efficiency and safety of train operation. However, most fault …

We provide a critical outlook of the evolution of Industrial Process Monitoring (IPM) since its
introduction almost 100 years ago. Several evolution trends that have been structuring IPM …

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 …

PLS-regression (PLSR) is the PLS approach in its simplest, and in chemistry and
technology, most used form (two-block predictive PLS). PLSR is a method for relating two …

The viewpoint taken in much of this text is that PCA is mainly a descriptive tool with no need
for rigorous distributional or model assumptions. This implies that it can be used on a wide …

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 …

Chemical processes are in general subject to time variant conditions because of load
changes, product grade transitions, or other causes, resulting in typical nonstationary …

Noise and high-dimension of process signals decrease effectiveness of those regular fault
detection and diagnosis models in multivariate processes. Deep learning technique shows …