Adaptive Fault Detection for Complex Dynamic Processes Based on JIT Updated Data Set
A novel fault detection technique is proposed to explicitly account for the nonlinear, dynamic, and multimodal problems existed in the practical and complex dynamic processes. Just-in-time (JIT) detection method and k-nearest neighbor (KNN) rule-based statistical process control (SPC) approach are i...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2012-01-01
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| Series: | Journal of Applied Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2012/809243 |
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| _version_ | 1849306527798657024 |
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| author | Jinna Li Yuan Li Haibin Yu Yanhong Xie Cheng Zhang |
| author_facet | Jinna Li Yuan Li Haibin Yu Yanhong Xie Cheng Zhang |
| author_sort | Jinna Li |
| collection | DOAJ |
| description | A novel fault detection technique is proposed to explicitly account for the nonlinear, dynamic, and multimodal problems existed in the practical and complex dynamic processes. Just-in-time (JIT) detection method and k-nearest neighbor (KNN) rule-based statistical process control (SPC) approach are integrated to construct a flexible and adaptive detection scheme for the control process with nonlinear, dynamic, and multimodal cases. Mahalanobis distance, representing the correlation among samples, is used to simplify and update the raw data set, which is the first merit in this paper. Based on it, the control limit is computed in terms of both KNN rule and SPC method, such that we can identify whether the current data is normal or not by online approach. Noted that the control limit obtained changes with updating database such that an adaptive fault detection technique that can effectively eliminate the impact of data drift and shift on the performance of detection process is obtained, which is the second merit in this paper. The efficiency of the developed method is demonstrated by the numerical examples and an industrial case. |
| format | Article |
| id | doaj-art-2cddec15983847f7a09df49e6938bab0 |
| institution | Kabale University |
| issn | 1110-757X 1687-0042 |
| language | English |
| publishDate | 2012-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Applied Mathematics |
| spelling | doaj-art-2cddec15983847f7a09df49e6938bab02025-08-20T03:55:02ZengWileyJournal of Applied Mathematics1110-757X1687-00422012-01-01201210.1155/2012/809243809243Adaptive Fault Detection for Complex Dynamic Processes Based on JIT Updated Data SetJinna Li0Yuan Li1Haibin Yu2Yanhong Xie3Cheng Zhang4Department of Science, Shenyang University of Chemical Technology, Liaoning, Shenyang 110142, ChinaCollege of Information Engineering, Shenyang University of Chemical Technology, Liaoning, Shenyang 110142, ChinaLab of Industrial Control Networks and Systems, Shenyang Institute of Automation, Chinese Academy of Sciences, Liaoning, Shenyang 110016, ChinaDepartment of Science, Shenyang University of Chemical Technology, Liaoning, Shenyang 110142, ChinaDepartment of Science, Shenyang University of Chemical Technology, Liaoning, Shenyang 110142, ChinaA novel fault detection technique is proposed to explicitly account for the nonlinear, dynamic, and multimodal problems existed in the practical and complex dynamic processes. Just-in-time (JIT) detection method and k-nearest neighbor (KNN) rule-based statistical process control (SPC) approach are integrated to construct a flexible and adaptive detection scheme for the control process with nonlinear, dynamic, and multimodal cases. Mahalanobis distance, representing the correlation among samples, is used to simplify and update the raw data set, which is the first merit in this paper. Based on it, the control limit is computed in terms of both KNN rule and SPC method, such that we can identify whether the current data is normal or not by online approach. Noted that the control limit obtained changes with updating database such that an adaptive fault detection technique that can effectively eliminate the impact of data drift and shift on the performance of detection process is obtained, which is the second merit in this paper. The efficiency of the developed method is demonstrated by the numerical examples and an industrial case.http://dx.doi.org/10.1155/2012/809243 |
| spellingShingle | Jinna Li Yuan Li Haibin Yu Yanhong Xie Cheng Zhang Adaptive Fault Detection for Complex Dynamic Processes Based on JIT Updated Data Set Journal of Applied Mathematics |
| title | Adaptive Fault Detection for Complex Dynamic Processes Based on JIT Updated Data Set |
| title_full | Adaptive Fault Detection for Complex Dynamic Processes Based on JIT Updated Data Set |
| title_fullStr | Adaptive Fault Detection for Complex Dynamic Processes Based on JIT Updated Data Set |
| title_full_unstemmed | Adaptive Fault Detection for Complex Dynamic Processes Based on JIT Updated Data Set |
| title_short | Adaptive Fault Detection for Complex Dynamic Processes Based on JIT Updated Data Set |
| title_sort | adaptive fault detection for complex dynamic processes based on jit updated data set |
| url | http://dx.doi.org/10.1155/2012/809243 |
| work_keys_str_mv | AT jinnali adaptivefaultdetectionforcomplexdynamicprocessesbasedonjitupdateddataset AT yuanli adaptivefaultdetectionforcomplexdynamicprocessesbasedonjitupdateddataset AT haibinyu adaptivefaultdetectionforcomplexdynamicprocessesbasedonjitupdateddataset AT yanhongxie adaptivefaultdetectionforcomplexdynamicprocessesbasedonjitupdateddataset AT chengzhang adaptivefaultdetectionforcomplexdynamicprocessesbasedonjitupdateddataset |