東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Fault Detection Based on Mean-Shift ...

Carson, David.

FindBook

Google Book

Amazon

博客來

Fault Detection Based on Mean-Shift Clustering and Immune Danger Theory.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Fault Detection Based on Mean-Shift Clustering and Immune Danger Theory./
作者:	Carson, David.
面頁冊數:	185 p.
附註:	Source: Dissertation Abstracts International, Volume: 77-03(E), Section: B.
Contained By:	Dissertation Abstracts International77-03B(E).
標題:	Electrical engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3731933
ISBN:	9781339185170

Fault Detection Based on Mean-Shift Clustering and Immune Danger Theory.
Carson, David.

Fault Detection Based on Mean-Shift Clustering and Immune Danger Theory. - 185 p.

Source: Dissertation Abstracts International, Volume: 77-03(E), Section: B.

Thesis (Ph.D.)--The George Washington University, 2016.

Modern control systems rely on a complex network of interacting sub-systems. Because a failure in any of these sub-systems could have catastrophic consequences, it is necessary to detect and isolate faults in control system components, i.e. actuators or sensors before the fault is allowed to cause a system failure. Early detection and isolation could enable timely system reconfiguration and increase safety of operations. In the literature, there are numerous fault detection and isolation techniques presented. The central challenge with fault detection is determining the difference between normal and potentially harmful activities in the system. This thesis demonstrates the ability of one technique, based on mean-shift clustering and immune danger theory, to detect and isolate faults.

ISBN: 9781339185170Subjects--Topical Terms:

649834
Electrical engineering.

Fault Detection Based on Mean-Shift Clustering and Immune Danger Theory.
LDR:03107nmm a2200349 4500 001 2067060
005 20160321080127.5
008 170521s2016 ||||||||||||||||| ||eng d
020 $a 9781339185170
035 $a (MiAaPQ)AAI3731933
035 $a AAI3731933
040 $a MiAaPQ $c MiAaPQ
100 1 $a Carson, David. $3 667046
245 1 0 $a Fault Detection Based on Mean-Shift Clustering and Immune Danger Theory.
300 $a 185 p.
500 $a Source: Dissertation Abstracts International, Volume: 77-03(E), Section: B.
500 $a Adviser: Robert L. Carroll.
502 $a Thesis (Ph.D.)--The George Washington University, 2016.
520 $a Modern control systems rely on a complex network of interacting sub-systems. Because a failure in any of these sub-systems could have catastrophic consequences, it is necessary to detect and isolate faults in control system components, i.e. actuators or sensors before the fault is allowed to cause a system failure. Early detection and isolation could enable timely system reconfiguration and increase safety of operations. In the literature, there are numerous fault detection and isolation techniques presented. The central challenge with fault detection is determining the difference between normal and potentially harmful activities in the system. This thesis demonstrates the ability of one technique, based on mean-shift clustering and immune danger theory, to detect and isolate faults.
520 $a The simulations and experiments for multiple sensor systems have confirmed the ability of the new approach for online fusing and fault detection. The hybrid system provides fault tolerance by handling different problems such as noisy sensor signals and multiple faulty sensors. This makes the hybrid approach attractive for solving such fusion and fault detection problems during real-time operations.
520 $a The contributions of this thesis are summarized as follows:
520 $a • The thesis proved that the Mean-Shift (MS) algorithm with isolated stationary points generates a convergent sequence. This thesis also studied special one- dimensional case and showed that in this case the MS algorithm generates a monotone and convergent sequence with both analytic and non-analytic kernels. Furthermore, a slightly modified version of the MS algorithm was proposed in order to improve the performance of the generated mode estimate sequence.
520 $a • The effectiveness of the modified MS algorithm was examined on three data sets - closed, sparse and two cluster.
520 $a • A fusion algorithm using MS and Fuzzy-C means (FCM) was presented.
520 $a • A fault detection algorithm was generated from the immune danger theory.
520 $a • Combining the aforementioned algorithms produced an efficient, hybrid approach for multiple sensor data fusion and fault detection.
590 $a School code: 0075.
650 4 $a Electrical engineering. $3 649834
690 $a 0544
710 2 $a The George Washington University. $b Electrical Engineering. $3 1035543
773 0 $t Dissertation Abstracts International $g 77-03B(E).
790 $a 0075
791 $a Ph.D.
792 $a 2016
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3731933