Fault Detection Strategy for Fork Displacement Sensor in Dual Clutch Transmission via Deep Long Short-Term Memory Network

Abstract

Detecting the fork displacement sensor fault is critical for ensuring the reliability and safety of a dual clutch transmission (DCT). In this paper, a deep learning method is proposed to monitor the state of the fork displacement sensor. Firstly, the fork displacement prediction algorithm is developed based on the deep long short-term memory (LSTM) network using the driving data of a DCT vehicle. Secondly, the synchronizer control system model is constructed to imitate the fork displacement sensor fault as the experimental vehicle works in normal condition and the collected data lacks of faulty sensor signal. Finally, the residual is obtained by comparing the predicted fork displacement and the measured sensor information. The sensor fault is detected as the residual exceeds the predetermined threshold. Results show the fork displacement prediction algorithm can accurately estimate the synchronizer position. And the fault detection method can detect the fork displacement sensor fault timely and accurately.