| Title |
Introduction Of Watchdog Prognostics Agent And Its Application To Elevator Hoistway Performance Assessment |
| ID_Doc |
75336 |
| Authors |
Yan, JH; Lee, J; Pan, YC |
| Title |
Introduction Of Watchdog Prognostics Agent And Its Application To Elevator Hoistway Performance Assessment |
| Year |
2005 |
| Published |
Journal Of Industrial And Production Engineering, 22, 1 |
| DOI |
10.1080/10170660509509277 |
| Abstract |
Today's competition in industry depends not just on lean manufacturing, but also on the ability to provide customers with accountable life-cycle support for sustainable value. To improve customer service responsiveness and aftermarket business efficiency, new service business model for enable products and systems to achieve near-zero unscheduled downtime has been adopted by many companies. This transformation necessitates the depolyment of smart prognsotics tools to predict and prevent possible failures before they occur. This paper introduces an innovative approach in using Watchdog Agent (TM) for machine degradation and failure prognostics. The methods of Watchdog Agent (TM) are developed to use multi-sensor information from product for performance degradation assessment and life prediction. Specially, the logistic regression (LR) method is introduced and applied to an elevator hoistway performance aseessment. The logistic regression model is designed for repeatable tracking of motor speed profile of elevator hoistway movement for on-line monitoring and prognostic purposes. Features such as acceleration time (interval from triggering elevator to reaching maximum speed), deceleration time (from maximum speed to elevator stop), as well as average maximum speed were used as inputs to Logistic Regression tool for performance assessment. The real application results show that the LR is a very promising methodology for machine degradation assessment. |
| Author Keywords |
prognostics; degradation assessment; life prediction; elevator maintenance |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Emerging Sources Citation Index (ESCI) |
| EID |
WOS:000218658800007 |
| WoS Category |
Engineering, Industrial |
| Research Area |
Engineering |
| PDF |
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