Dynamic fault diagnosis via variational Bayesian mixture factor analysis with application to wastewater treatment
XIAO Hong-jun
LIU Yi-qi
HUANG Dao-ping
Abstract：Exposure to variables coupled, significant nonlinearities, parameters shift and time delay in the wastewa-ter treatment processes often result in sensors unavailable and even the entire plant not to be optimized and diagnosed efficiently. Therefore, this work presents the design of a dynamic fault diagnosis method on the basis of the variational Bayesian mixture factor analysis (VBMFA) together with the dynamic data. Also, the mixture factors can be identified in a semi-adaptive way. The purpose of proposed methodologies is to capture strong nonlinearity and the significant dynamic feature of WWTPs, which seriously limit the application of conventional multivariate statistical methods for fault diagnosis implementation. The performance of our proposed method is validated through a simulation study at BSM1. 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.
Keywords：fault diagnosiswastewater treatmentvariational Bayesian learningmixture factor analysissemi-adaptive
Publication Date：2016-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：8( 1519-1526 )