A mixture of variational canonical correlation analysis for nonlinear and quality-relevant process monitoring

Tools

Liu, Yiqi and Liu, Bin and Zhao, Xiujie and Xie, Min (2018) A mixture of variational canonical correlation analysis for nonlinear and quality-relevant process monitoring. IEEE Transactions on Industrial Electronics, 65 (8). pp. 6478-6486. ISSN 0278-0046 (https://doi.org/10.1109/TIE.2017.2786253)

[thumbnail of Liu-etal-IEEE-TIE-2017-A-mixture-of-variational-canonical-correlation-analysis]
Preview
 Text. Filename: Liu_etal_IEEE_TIE_2017_A_mixture_of_variational_canonical_correlation_analysis.pdf
Accepted Author Manuscript
Download (1MB)| Preview

Abstract

Proper monitoring of quality-related variables in industrial processes is nowadays one of the main worldwide challenges with significant safety and efficiency implications.Variational Bayesian mixture of canonical correlation analysis (VBMCCA)-based process monitoring method was proposed in this paper to predict and diagnose these hard-to-measure quality-related variables simultaneously. Use of Student's t-distribution, rather than Gaussian distribution, in the VBMCCA model makes the proposed process monitoring scheme insensitive to disturbances, measurement noises, and model discrepancies. A sequential perturbation (SP) method together with derived parameter distribution of VBMCCA is employed to approach the uncertainty levels, which is able to provide a confidence interval around the predicted values and give additional control line, rather than just a certain absolute control limit, for process monitoring. The proposed process monitoring framework has been validated in a wastewater treatment plant (WWTP) simulated by benchmark simulation model with abrupt changes imposing on a sensor and a real WWTP with filamentous sludge bulking. The results show that the proposed methodology is capable of detecting sensor faults and process faults with satisfactory accuracy.

ORCID iDs

Liu, Yiqi, Liu, Bin ORCID logoORCID: https://orcid.org/0000-0002-3946-8124, Zhao, Xiujie and Xie, Min;
  • Item type:Article
    ID code:66872
    Dates:
    Date
    Event
    31 August 2018
    Published
    25 December 2017
    Published Online
    10 December 2017
    Accepted
    Notes:© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Subjects:Technology > Electrical engineering. Electronics Nuclear engineering
    Department:Strathclyde Business School > Management Science
    Depositing user: Pure Administrator
    Date deposited:08 Feb 2019 13:08
    Last modified:03 Dec 2024 20:49
    Related URLs:
    URI:https://strathprints.strath.ac.uk/id/eprint/66872
CORE (COnnecting REpositories)