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Model-based and data driven fault di...

Yang, Qingsong.

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Model-based and data driven fault diagnosis methods with applications to process monitoring.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Model-based and data driven fault diagnosis methods with applications to process monitoring./
作者:	Yang, Qingsong.
面頁冊數:	203 p.
附註:	Source: Dissertation Abstracts International, Volume: 65-01, Section: B, page: 0418.
Contained By:	Dissertation Abstracts International65-01B.
標題:	Engineering, System Science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3119592

Model-based and data driven fault diagnosis methods with applications to process monitoring.
Yang, Qingsong.

Model-based and data driven fault diagnosis methods with applications to process monitoring. - 203 p.

Source: Dissertation Abstracts International, Volume: 65-01, Section: B, page: 0418.

Thesis (Ph.D.)--Case Western Reserve University, 2004.

This thesis discusses statistical model-based as well as data driven fault diagnosis approaches with applications to process monitoring.Subjects--Topical Terms:

1018128
Engineering, System Science.

Model-based and data driven fault diagnosis methods with applications to process monitoring.
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245 1 0 $a Model-based and data driven fault diagnosis methods with applications to process monitoring.
300 $a 203 p.
500 $a Source: Dissertation Abstracts International, Volume: 65-01, Section: B, page: 0418.
500 $a Adviser: Kenneth A. Loparo.
502 $a Thesis (Ph.D.)--Case Western Reserve University, 2004.
520 $a This thesis discusses statistical model-based as well as data driven fault diagnosis approaches with applications to process monitoring.
520 $a A model-based technique, the Multiple Model Extended Kalman Filter (MMEKF), is proposed for the fault diagnosis of nonlinear stochastic systems. The MMEKF system consists of a bank of EKFs, where each filter is tuned to a specific fault. The res
520 $a Simulation results on three different kinds of faults, actuator, sensor and process fault, show that the MMEKF fault diagnosis system can successfully perform the fault detection and isolation task. The fault magnitude can also be estimated by P
520 $a Process monitoring based on PCA has been a very popular topic in the context of data driven fault diagnosis techniques in recent years. However, two fundamental statistical assumptions have limited the performance of the conventional PCA approac
520 $a An industrial application for the Adaptive PCA and simulation studies for the MPCA and MSPCA show superior performance over the conventional PCA.
520 $a In addition, these approaches are also expected to improve the robustness of residual evaluation for the MMEKF approach when the effect of model uncertainty and unmodeled dynamics and disturbance cause the residuals to deviate from the assumed w
590 $a School code: 0042.
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790 1 0 $a Loparo, Kenneth A., $e advisor
790 $a 0042
791 $a Ph.D.
792 $a 2004
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