Research Area: Machine Learning
In the era of the 4th industrial revolution, a key challenge for the industries is the efficient reduction of the production cost caused by malfunctioning equipment. This paper proposes a Fault Detection and Diagnosis (FDD) framework for Non-Linear Processes utilizing Dynamic Neural Networks and feature reduction methods. We investigate both types of dynamic neural models, ie. Recurrent Neural Networks -in particular Long Short-Term Memory (LSTM) models, and Time Delay Neural Networks (TDNN). Intending to mitigate the overfitting problem, we also investigated the use of feature reduction techniques such as Non-Negative Matrix Factorization (NMF), Principal Component Analysis (PCA), and kernel PCA (kPCA), as preprocessing steps in our Machine Learning pipeline. The Tennessee Eastman Process (TEP) is used to evaluate our proposed framework on 18 different faults. Our simulations demonstrate that our method outperforms state of the art methods in the majority of those faults.
Keywords:
Fault Detection And Diagnosis
Non-Linear Processes
Dynamic Neural Networks
Long Short-Term Memory (LSTM)
Time Delay Neural Networks (TDNN)
Principal Component Analysis
Deep Learning
Machine Learning
Author(s) Name: Georgios Gravanis, Ioannis Dragogias, Konstantinos Papakiriakos, Chrysovalantou Ziogou, Konstantinos Diamantaras
Journal name: Computers & Chemical Engineering
Conferrence name:
Publisher name: Elsevier
DOI: 10.1016/j.compchemeng.2021.107531
Volume Information: Volume 156, January 2022, 107531
