Detection of Emerging Faults on Industrial Gas Turbines Using Extended Gaussian Mixture Models
This paper extends traditional Gaussian mixture model (GMM) techniques to provide recognition of operational states and detection of emerging faults for industrial systems. A variational Bayesian method allows a GMM to cluster with its mixture components to facilitate the extraction of steady-state...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2017-01-01
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| Series: | International Journal of Rotating Machinery |
| Online Access: | http://dx.doi.org/10.1155/2017/5435794 |
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| _version_ | 1832554604405981184 |
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| author | Yu Zhang Chris Bingham Miguel Martínez-García Darren Cox |
| author_facet | Yu Zhang Chris Bingham Miguel Martínez-García Darren Cox |
| author_sort | Yu Zhang |
| collection | DOAJ |
| description | This paper extends traditional Gaussian mixture model (GMM) techniques to provide recognition of operational states and detection of emerging faults for industrial systems. A variational Bayesian method allows a GMM to cluster with its mixture components to facilitate the extraction of steady-state operational behaviour; this is recognised as being a primary factor in reducing the susceptibility of alternative prognostic/diagnostic techniques, which would initiate false-alarms resulting from control set-point and load changes. Furthermore, a GMM with an outlier component is discussed and applied for direct novelty/fault detection. An advantage of the variational Bayesian method over traditional predefined thresholds is the extraction of steady-state data during both full- and part-load cases, and a primary advantage of the GMM with an outlier component is its applicability for novelty detection when there is a lack of prior knowledge of fault patterns. Results obtained from the real-time measurements on the operational industrial gas turbines have shown that the proposed technique provides integrated preprocessing, benchmarking, and novelty/fault detection methodology. |
| format | Article |
| id | doaj-art-ef82855420e64d089014f37ff556832c |
| institution | Kabale University |
| issn | 1023-621X 1542-3034 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Rotating Machinery |
| spelling | doaj-art-ef82855420e64d089014f37ff556832c2025-02-03T05:51:04ZengWileyInternational Journal of Rotating Machinery1023-621X1542-30342017-01-01201710.1155/2017/54357945435794Detection of Emerging Faults on Industrial Gas Turbines Using Extended Gaussian Mixture ModelsYu Zhang0Chris Bingham1Miguel Martínez-García2Darren Cox3School of Engineering, University of Lincoln, Lincoln LN6 7TS, UKSchool of Engineering, University of Lincoln, Lincoln LN6 7TS, UKSchool of Engineering, University of Lincoln, Lincoln LN6 7TS, UKSiemens Industrial Turbomachinery Ltd., Lincoln LN5 7FD, UKThis paper extends traditional Gaussian mixture model (GMM) techniques to provide recognition of operational states and detection of emerging faults for industrial systems. A variational Bayesian method allows a GMM to cluster with its mixture components to facilitate the extraction of steady-state operational behaviour; this is recognised as being a primary factor in reducing the susceptibility of alternative prognostic/diagnostic techniques, which would initiate false-alarms resulting from control set-point and load changes. Furthermore, a GMM with an outlier component is discussed and applied for direct novelty/fault detection. An advantage of the variational Bayesian method over traditional predefined thresholds is the extraction of steady-state data during both full- and part-load cases, and a primary advantage of the GMM with an outlier component is its applicability for novelty detection when there is a lack of prior knowledge of fault patterns. Results obtained from the real-time measurements on the operational industrial gas turbines have shown that the proposed technique provides integrated preprocessing, benchmarking, and novelty/fault detection methodology.http://dx.doi.org/10.1155/2017/5435794 |
| spellingShingle | Yu Zhang Chris Bingham Miguel Martínez-García Darren Cox Detection of Emerging Faults on Industrial Gas Turbines Using Extended Gaussian Mixture Models International Journal of Rotating Machinery |
| title | Detection of Emerging Faults on Industrial Gas Turbines Using Extended Gaussian Mixture Models |
| title_full | Detection of Emerging Faults on Industrial Gas Turbines Using Extended Gaussian Mixture Models |
| title_fullStr | Detection of Emerging Faults on Industrial Gas Turbines Using Extended Gaussian Mixture Models |
| title_full_unstemmed | Detection of Emerging Faults on Industrial Gas Turbines Using Extended Gaussian Mixture Models |
| title_short | Detection of Emerging Faults on Industrial Gas Turbines Using Extended Gaussian Mixture Models |
| title_sort | detection of emerging faults on industrial gas turbines using extended gaussian mixture models |
| url | http://dx.doi.org/10.1155/2017/5435794 |
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