Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Explainability: Relevance based Dynamic Deep Learning Algorithm for
  Fault Detection and Diagnosis in Chemical Processes   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.

28
The categories of this paper are: alg-geom
24
The categories of this paper are: dg-ga
3
The categories of this paper are: physics
12
The categories of this paper are: nucl-th
16
The categories of this paper are: stat
31
The categories of this paper are: ao-sci
2
The categories of this paper are: math
20
The categories of this paper are: acc-phys
33
The categories of this paper are: plasm-ph
13
The categories of this paper are: quant-ph
22
The categories of this paper are: chao-dyn
21
The categories of this paper are: adap-org
0
The categories of this paper are: hep-ph
32
The categories of this paper are: atom-ph
10
The categories of this paper are: q-bio
23
The categories of this paper are: solv-int