Kalman-Filtering-Based Prognostics for Automatic Transmission Clutches

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.