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Development of Integrated Prognostic...

Ma, Jinghua.

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Development of Integrated Prognostics: Application to Bearing and Bevel Gear Life Prediction.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Development of Integrated Prognostics: Application to Bearing and Bevel Gear Life Prediction./
作者:	Ma, Jinghua.
面頁冊數:	157 p.
附註:	Source: Dissertation Abstracts International, Volume: 74-05(E), Section: B.
Contained By:	Dissertation Abstracts International74-05B(E).
標題:	Engineering, Industrial. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3550923
ISBN:	9781267883322

Development of Integrated Prognostics: Application to Bearing and Bevel Gear Life Prediction.
Ma, Jinghua.

Development of Integrated Prognostics: Application to Bearing and Bevel Gear Life Prediction. - 157 p.

Source: Dissertation Abstracts International, Volume: 74-05(E), Section: B.

Thesis (Ph.D.)--University of Illinois at Chicago, 2012.

Prognosis and health management (PHM) for complex systems have become more and more important when the economic impact of reliability related issues and the cost effective operation of critical assets is rapidly increasing. Current maintenance strategies have progressed from periodical maintenance and break down maintenance, to preventive maintenance, then to condition-based maintenance (CBM). CBM is based on using real-time data to prioritize and optimize maintenance resources. Prognosis as the most important part of CBM is becoming more and more important in these fields such as aeronautics and astronautics. The objective of this dissertation is to develop an integrated machinery prognostic methodology based on particle filtering and validate the developed prognostic methodology using real industrial case studies.

ISBN: 9781267883322Subjects--Topical Terms:

626639
Engineering, Industrial.

Development of Integrated Prognostics: Application to Bearing and Bevel Gear Life Prediction.
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100 1 $a Ma, Jinghua. $3 2092208
245 1 0 $a Development of Integrated Prognostics: Application to Bearing and Bevel Gear Life Prediction.
300 $a 157 p.
500 $a Source: Dissertation Abstracts International, Volume: 74-05(E), Section: B.
500 $a Adviser: David He.
502 $a Thesis (Ph.D.)--University of Illinois at Chicago, 2012.
520 $a Prognosis and health management (PHM) for complex systems have become more and more important when the economic impact of reliability related issues and the cost effective operation of critical assets is rapidly increasing. Current maintenance strategies have progressed from periodical maintenance and break down maintenance, to preventive maintenance, then to condition-based maintenance (CBM). CBM is based on using real-time data to prioritize and optimize maintenance resources. Prognosis as the most important part of CBM is becoming more and more important in these fields such as aeronautics and astronautics. The objective of this dissertation is to develop an integrated machinery prognostic methodology based on particle filtering and validate the developed prognostic methodology using real industrial case studies.
520 $a In this dissertation, an integrated machinery prognostic methodology based on particle filtering has been developed. In the development of the proposed prognostic methodology in this research, these three fundamental issues have been addressed: (1) defining the state transition function using a data mining approach; (2) integrating a one-dimensional health index (HI) into particle filtering to define the measurement function; (3) developing an l-step ahead RUL estimator incorporating with a measure of the associated error. The developed prognostic methodology has been validated using three industrial case studies. The first case study concerns steel bearing prognosis and remaining useful life prediction. The second case study concerns spiral bevel gear prognosis and RUL prediction. In the last case study, the ground truth data of hybrid ceramic bearings are used to validate the methodology.
520 $a The specifically contributions of the dissertation are summarized as follows:
520 $a (1) An integrated particle filtering algorithm was developed in which a one-dimensional HI was integrated into particle filtering to define the observation parameters.
520 $a (2) Instead of using Paris' Law, data mining algorithm was used to build the state function.
520 $a (3) Data mining based approaches were used to build the observation function.
520 $a (4) An l-step ahead state parameter prediction and RUL estimator was developed.
520 $a (5) The presented prognostics method has been validated using data from steel bearing, hybrid ceramic bearing and spiral bevel gear case studies.
590 $a School code: 0799.
650 4 $a Engineering, Industrial. $3 626639
650 4 $a Engineering, Mechanical. $3 783786
650 4 $a Engineering, System Science. $3 1018128
690 $a 0546
690 $a 0548
690 $a 0790
710 2 $a University of Illinois at Chicago. $b Mechanical and Industrial Engineering. $3 2092209
773 0 $t Dissertation Abstracts International $g 74-05B(E).
790 1 0 $a He, David, $e advisor
790 1 0 $a He, David $e committee member
790 1 0 $a Cetinkunt, Sabri $e committee member
790 1 0 $a Darabi, Houshang $e committee member
790 1 0 $a Scott, Michael $e committee member
790 1 0 $a Liu, Derong $e committee member
790 1 0 $a Dempsey, Paula $e committee member
790 $a 0799
791 $a Ph.D.
792 $a 2012
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3550923