This paper focuses on the Tennessee Eastman (TE) process and for the first time investigates it in a cognitive way. The cognitive fault diagnosis does not assume prior knowledge of the fault numbers and signatures. This approach firstly employs deterministic reservoir models to fit the multiple-input and multiple-output signals in the TE process, which map the signal space to the (reservoir) mo...

In order to better utilize historical process data from faulty operations, supervised learning methods, such as Fisher discriminant analysis (FDA), have been adopted in process monitoring. However, such methods can only separate known faults from normal operations, and they have no means to deal with unknown faults. In addition, most of these methods are not designed for handling non-Gaussian d...

Abstract The previous chapters have described the mathematical principles and algorithms of multivariate statistical methods, as well monitoring processes when used for fault diagnosis. In order to validate effectiveness data-driven analysis methods in field diagnosis, it is necessary conduct corresponding experiments. Therefore this chapter introduces two kinds simulation platform, Tennessee E...