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Quantum Physics

arXiv:2003.00264 (quant-ph)
[Submitted on 29 Feb 2020 (v1), last revised 2 Oct 2020 (this version, v2)]

Title:Quantum Computing Assisted Deep Learning for Fault Detection and Diagnosis in Industrial Process Systems

Authors:Akshay Ajagekar, Fengqi You
View a PDF of the paper titled Quantum Computing Assisted Deep Learning for Fault Detection and Diagnosis in Industrial Process Systems, by Akshay Ajagekar and 1 other authors
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Abstract:Quantum computing (QC) and deep learning techniques have attracted widespread attention in the recent years. This paper proposes QC-based deep learning methods for fault diagnosis that exploit their unique capabilities to overcome the computational challenges faced by conventional data-driven approaches performed on classical computers. Deep belief networks are integrated into the proposed fault diagnosis model and are used to extract features at different levels for normal and faulty process operations. The QC-based fault diagnosis model uses a quantum computing assisted generative training process followed by discriminative training to address the shortcomings of classical algorithms. To demonstrate its applicability and efficiency, the proposed fault diagnosis method is applied to process monitoring of continuous stirred tank reactor (CSTR) and Tennessee Eastman (TE) process. The proposed QC-based deep learning approach enjoys superior fault detection and diagnosis performance with obtained average fault detection rates of 79.2% and 99.39% for CSTR and TE process, respectively.
Subjects: Quantum Physics (quant-ph); Machine Learning (cs.LG); Systems and Control (eess.SY); Optimization and Control (math.OC); Machine Learning (stat.ML)
Cite as: arXiv:2003.00264 [quant-ph]
  (or arXiv:2003.00264v2 [quant-ph] for this version)
  https://doi.org/10.48550/arXiv.2003.00264
arXiv-issued DOI via DataCite
Journal reference: Comp. Chem. Eng., 143 (2020), pp. 107119
Related DOI: https://doi.org/10.1016/j.compchemeng.2020.107119
DOI(s) linking to related resources

Submission history

From: Fengqi You [view email]
[v1] Sat, 29 Feb 2020 14:18:33 UTC (2,255 KB)
[v2] Fri, 2 Oct 2020 02:44:48 UTC (1,209 KB)
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