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首页> 外文期刊>Mechatronics, IEEE/ASME Transactions on >Kalman-Filtering-Based Prognostics for Automatic Transmission Clutches
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Kalman-Filtering-Based Prognostics for Automatic Transmission Clutches

机译：基于卡尔曼滤波的自动变速器离合器预测

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摘要

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.
机译：自最近几年以来，工业对自动变速器离合器的低成本诊断工具的需求（即，基于来自通常可用的传感器的测量数据）已经增长。在本文中，通过在扩展的卡尔曼滤波框架下将新开发的降级模型与可测量的预锁定功能融合在一起，开发了一种预测工具。由于可以从湿式离合器应用中通常可用的感觉数据中提取此功能，因此，开发的预测工具不需要任何其他传感器的额外费用。通过使用完全仪器化的SAE＃2测试装置通过加速寿命测试获得的市售湿式离合器的新历史数据已获得并处理。实验结果表明，本文开发的预测算法优于早期开发的基于加权平均斜率方法（即数据驱动方法）的预测算法。结果表明，与早期开发的预测算法相比，采用新型预测算法的离合器剩余使用寿命估计值保持在20％的所需精度范围内，不确定性间隔相对较小。

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