Unsupervised Anomaly Detection Approach for Shift Quality Assessment Using Deep Neural Networks

It is necessary to calibrate the hydraulic pressure of the shift control to develop an automatic transmission (AT), and this calibration process entails a subjective shift quality assessment by experienced engineers. An objective shift quality assessment methodology has been explored for a long time to replace the engineer. The most recent data-based assessment model has attained a nearly human-like performance. However, preparing the large number of data labels required for supervised learning of the model has limitations. This study proposes an unsupervised anomaly detection model for objective shift quality assessment to address data label shortages and high data labeling costs. The proposed anomaly detection model is trained to classify a normal shift and an abnormal shift using just normal shift data. It is possible to easily obtain many train datasets from ordinary vehicles, and data labeling is not required. On the basis of real vehicle shift data, multiple anomaly detection models composed of various deep neural networks are developed and assessed. The evaluation results show that training exclusively on normal shift data can detect abnormal shifts; the best area under receiver operating characteristic curve is 0.902.