Data-driven supervised fault diagnosis methods based on latent variable models: a comparative study

Abstract

[EN] A comparison among widely used multivariate latent variable-based techniques for supervised process fault diagnosis was carried out.

In order to assess their overall performance several diagnosis criteria were proposed (C-1: most suspected fault assignment; C-2: threshold-based fault assignment). Additionally, it was evaluated i) how the size of the training set used to build the latent variable models affected the diagnosis ability of the methods under study, ii) how they behaved under new types of failures not included in the original list of fault candidates and iii) which of them were more suitable for either early or late diagnosis.

To accomplish all these objectives, the approaches were tested in different scenarios. Two datasets were analysed: the first was generated by a Simulink-based model of a binary distillation column, while the second relates to a pasteurisation process performed in a laboratory-scale plant.