Recognizing similarities in automatic transmissions of vehicles by using time series data and autoencorders 2019-01-0343
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.