Fault detection and identification using Bayesian recurrent neural networks
Name
1911.04386.pdf
Description
Accepted version
Size
5.88 MB
Format
Adobe PDF
Checksum (MD5)
cfa400c3d0dfaad35e957d04700eb3a5
Author(s)
Sun, Weike
Paiva, Antonio RC
Xu, Peng
Sundaram, Anantha
Braatz, Richard D
Date Issued
2020
Journal
Computers and Chemical Engineering
Publisher
Elsevier BV
Version
Author's final manuscript
Abstract
© 2020 In the processing and manufacturing industries, there has been a large push to produce higher quality products and ensure maximum efficiency of processes, which requires approaches to effectively detect and resolve disturbances to ensure optimal operations. While many types of disturbances can be compensated by a control system, it cannot handle some large process disruptions. As such, it is important to develop monitoring systems to effectively detect and identify those faults such that they can be quickly resolved by operators. This article proposes a novel probabilistic fault detection and identification method which adopts a newly developed deep learning approach using Bayesian recurrent neural networks (BRNNs) with variational dropout. The BRNN model is general and can model complex nonlinear dynamics. Moreover, compared to traditional statistic-based data-driven fault detection and identification methods, the proposed BRNN-based method yields uncertainty estimates which allow for simultaneous fault detection of chemical processes, direct fault identification, and fault propagation analysis. The performance of the method is demonstrated and contrasted to (dynamic) principal component analysis, which is widely applied in the industry, in the benchmark Tennessee Eastman process (TEP) and a real chemical manufacturing dataset.
MIT Department
Massachusetts Institute of Technology. Department of Chemical Engineering
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1016/J.COMPCHEMENG.2020.106991
