A propagation path-based interpretable neural network model for fault detection and diagnosis in chemical process systems

Author:

Nguyen BenjaminORCID,Chioua MoncefORCID

Funder

Institut de Valorisation des Données

Natural Sciences and Engineering Research Council of Canada

Publisher

Elsevier BV

Reference72 articles.

1. Explainability: Relevance based dynamic deep learning algorithm for fault detection and diagnosis in chemical processes;Agarwal;Computers & Chemical Engineering,2021

2. On the robustness of interpretability methods;Alvarez-Melis,2018

3. On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation;Bach;PLoS One,2015

4. FS-SCF network: Neural network interpretability based on counterfactual generation and feature selection for fault diagnosis;Barraza;Expert Systems with Applications,2024

5. Relational inductive biases, deep learning, and graph networks;Battaglia,2018

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