Decentralized fault detection and diagnosis via sparse PCA based decomposition and Maximum Entropy decision fusion
Journal
Journal of Process Control
Journal Volume
22
Journal Issue
4
Pages
738-750
Date Issued
2012
Author(s)
Abstract
This paper proposes an approach for decentralized fault detection and diagnosis in process monitoring sensor networks. The sensor network is decomposed into multiple, potentially overlapping, blocks using the Sparse Principal Component Analysis algorithm. Local predictions are generated at each block using Support Vector Machine classifiers. The local predictions are then fused via a Maximum Entropy algorithm. Empirical studies on the benchmark Tennessee Eastman Process data demonstrated that the proposed decentralized approach achieves accuracy comparable to that of the fully centralized approach, while offering benefits in terms of fault tolerance, reusability, and scalability. © 2012 Elsevier Ltd. All rights reserved.
SDGs
Type
journal article