Fault detection and diagnosis to enhance safety in digitalized process system

Journal article (2022)

Authors

Alibek Kopbayev Memorial University of Newfoundland
Faisal Khan Texas A&M University
M. Yang Safety and Security Science - TPM
Syeda Zohra Halim Texas A&M University
Research Group
Safety and Security Science (TPM) (TU Delft)
Copyright: © 2022 Alibek Kopbayev, Faisal Khan, M. Yang, S. Zohra Halim
DOI: https://doi.org/10.1016/j.compchemeng.2021.107609
Deep Neural Networks Fault detection and diagnosis Hybrid model KPCA Process system safety
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Published Date

2022

Language

English

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Faculty

Technology, Policy and Management

Department

Values Technology and Innovation

Research Group

Safety and Security Science

Abstract

The increased complexity of digitalized process systems requires advanced tools to detect and diagnose faults early to maintain safe operations. This study proposed a hybrid model that consists of Kernel Principal Component Analysis (kPCA) and DNNs that can be applied to detect and diagnose faults in various processes. The complex data is processed by kPCA to reduce its dimensionality; then, simplified data is used for two separate DNNs for training (detection and diagnosis). The relative performance of the hybrid model is compared with conventional methods. Tennessee Eastman Process was used to confirm the efficacy of the model. The results show the reduction of input dimensionality increases classification accuracy. In addition, splitting detection and diagnosis into two DNNs results in reduced training times and increased classification accuracy. The proposed hybrid model serves as an important tool to detect the fault and take early corrective actions, thus enhancing process safety.