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