Kalman-Filtering-Based Prognostics for Automatic Transmission Clutches

• Published in: IEEE/ASME transactions on mechatronics Vol. 21; no. 1; pp. 419 - 430
• Main Authors: Ompusunggu, Agusmian Partogi, Papy, Jean-Michel, Vandenplas, Steve
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-02-2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accelerated life tests; Algorithms; Automatic transmissions; Clutches; Condition monitoring; Degradation; Degradation model; Extended Kalman filter; Friction; IEEE transactions; Kalman filtering; Kalman filters; Mathematical model; Mechatronics; Prognostics and Health Management (PHM); Sensors; Slopes; Wet clutches; Wet friction clutches; extended Kalman filter (EKF); condition monitoring; degradation model; wet friction clutches; prognostics and health management (PHM); Automatic transmissions
• ISSN: 1083-4435; 1941-014X
• DOI: 10.1109/TMECH.2015.2440331

Demands of low-cost prognostics tool for automatic transmission clutches (i.e., based on measurement data from sensors typically available) by industry have increased since the last few years. In this paper, a prognostics tool is developed by fusing a newly developed degradation model with the measurable pre-lockup feature under the extended Kalman filtering framework. As this feature can be extracted from sensory data typically available in wet clutch applications, the developed prognostics tool, hence, does not require extra cost for any additional sensor. New history data of commercially available wet clutches obtained from accelerated life tests using a fully instrumented SAE#2 test setup have been acquired and processed. The experimental results show that the prognostics algorithm developed in this paper outperforms the early developed prognostics algorithm, which is based on the weighted mean slope method (i.e., data-driven approach). It is shown that the clutch remaining useful life estimations with the novel prognostics algorithm remain in the desired accuracy region of 20% with relatively small uncertainty interval in comparison with the early developed prognostics algorithm.