  The focus of this work is on Statistical Process Control (SPC) of a
manufacturing process based on available measurements. Two important
applications of SPC in industrial settings are fault detection and diagnosis
(FDD). In this work a deep learning (DL) based methodology is proposed for FDD.
We investigate the application of an explainability concept to enhance the FDD
accuracy of a deep neural network model trained with a data set of relatively
small number of samples. The explainability is quantified by a novel relevance
measure of input variables that is calculated from a Layerwise Relevance
Propagation (LRP) algorithm. It is shown that the relevances can be used to
discard redundant input feature vectors/ variables iteratively thus resulting
in reduced over-fitting of noisy data, increasing distinguishability between
output classes and superior FDD test accuracy. The efficacy of the proposed
method is demonstrated on the benchmark Tennessee Eastman Process.
