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Recognizing Similarities in Automatic Transmissions of Vehicles by Using Time Series Data and Autoencorders
Technical Paper
2019-01-0343
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
In recent years, the development time of vehicles has further accelerated, and automation of the development is an urgent task. One example of time wasting tasks is gear-shift calibration. For this purpose, Kawakami et al. have studied OK/NG classification of shift quality by using neural networks. However, their classifiers have a problem in versatility over different AT hardwares. In this paper, we develop autoencoders to realize similar/not-similar classification on three AT hardwares of vehicles. These hardwares have different lock-up multi/single-plate clutch structures. Experimental results show that the performance of similar/not-similar classification is high in terms of AUC.
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Citation
Kawakami, T., Ide, T., Tomita, K., Moriyama, E. et al., "Recognizing Similarities in Automatic Transmissions of Vehicles by Using Time Series Data and Autoencorders," SAE Technical Paper 2019-01-0343, 2019, https://doi.org/10.4271/2019-01-0343.Data Sets - Support Documents
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References
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