Semiadaptive Fault Diagnosis via Variational Bayesian Mixture Factor Analysis with Application to Wastewater Treatment. (21st March 2016)

• Record Type: Journal Article
• Authors: Xiao, Hongjun; Liu, Yiqi; Huang, Daoping
• Other Names: Katsura Seiichiro Academic Editor.
• Is Part Of: Journal of control science and engineering. Volume 2016(2016)
• Publication Date: 2016-03-21
• Subjects: Control theory -- Periodicals; 629.831205
• DOI: 10.1155/2016/2034826
• Languages: English
• ISSNs: 1687-5249

Abstract

Abstract : Mainly due to the hostile environment in wastewater plants (WWTPs), the reliability of sensors with respect to important qualities is often poor. In this work, we present the design of a semiadaptive fault diagnosis method based on the variational Bayesian mixture factor analysis (VBMFA) to support process monitoring. The proposed method is capable of capturing strong nonlinearity and the significant dynamic feature of WWTPs that seriously limit the application of conventional multivariate statistical methods for fault diagnosis implementation. The performance of proposed method is validated through a simulation study of a wastewater plant. Results have demonstrated that the proposed strategy can significantly improve the ability of fault diagnosis under fault-free scenario, accurately detect the abrupt change and drift fault, and even localize the root cause of corresponding fault properly.