Semiadaptive Fault Diagnosis via Variational Bayesian Mixture Factor Analysis with Application to Wastewater Treatment
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...
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| Format: | Article |
| Language: | English |
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Wiley
2016-01-01
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| Series: | Journal of Control Science and Engineering |
| Online Access: | http://dx.doi.org/10.1155/2016/2034826 |
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| _version_ | 1832556031428788224 |
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| author | Hongjun Xiao Yiqi Liu Daoping Huang |
| author_facet | Hongjun Xiao Yiqi Liu Daoping Huang |
| author_sort | Hongjun Xiao |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-f9320fe24f46453dbcff3fef452314a1 |
| institution | Kabale University |
| issn | 1687-5249 1687-5257 |
| language | English |
| publishDate | 2016-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Control Science and Engineering |
| spelling | doaj-art-f9320fe24f46453dbcff3fef452314a12025-02-03T05:46:38ZengWileyJournal of Control Science and Engineering1687-52491687-52572016-01-01201610.1155/2016/20348262034826Semiadaptive Fault Diagnosis via Variational Bayesian Mixture Factor Analysis with Application to Wastewater TreatmentHongjun Xiao0Yiqi Liu1Daoping Huang2School of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, ChinaSchool of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, ChinaSchool of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, ChinaMainly 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.http://dx.doi.org/10.1155/2016/2034826 |
| spellingShingle | Hongjun Xiao Yiqi Liu Daoping Huang Semiadaptive Fault Diagnosis via Variational Bayesian Mixture Factor Analysis with Application to Wastewater Treatment Journal of Control Science and Engineering |
| title | Semiadaptive Fault Diagnosis via Variational Bayesian Mixture Factor Analysis with Application to Wastewater Treatment |
| title_full | Semiadaptive Fault Diagnosis via Variational Bayesian Mixture Factor Analysis with Application to Wastewater Treatment |
| title_fullStr | Semiadaptive Fault Diagnosis via Variational Bayesian Mixture Factor Analysis with Application to Wastewater Treatment |
| title_full_unstemmed | Semiadaptive Fault Diagnosis via Variational Bayesian Mixture Factor Analysis with Application to Wastewater Treatment |
| title_short | Semiadaptive Fault Diagnosis via Variational Bayesian Mixture Factor Analysis with Application to Wastewater Treatment |
| title_sort | semiadaptive fault diagnosis via variational bayesian mixture factor analysis with application to wastewater treatment |
| url | http://dx.doi.org/10.1155/2016/2034826 |
| work_keys_str_mv | AT hongjunxiao semiadaptivefaultdiagnosisviavariationalbayesianmixturefactoranalysiswithapplicationtowastewatertreatment AT yiqiliu semiadaptivefaultdiagnosisviavariationalbayesianmixturefactoranalysiswithapplicationtowastewatertreatment AT daopinghuang semiadaptivefaultdiagnosisviavariationalbayesianmixturefactoranalysiswithapplicationtowastewatertreatment |