Hydraulic fault prediction of integrated transmission system based on LSTM

Abstract

Integrated drive-line fluids play a role in transmitting torque, providing control pressure, lubrication, and cleaning in the integrated drive-train. The increase of oil abrasive particles will accelerate the wear rate of rotating parts, while insufficient oil will lead to rapid ablation and gluing of rotating parts. According to statistics, more than 75% of hydraulic equipment failure is caused by oil pollution. Oil pollution leads to filter blockage which will cause fatal failure so that the entire transmission system cannot work. Therefore, it is extremely important to predict the fault and health management of the hydraulic system. This paper studies the oil fault of the integrated transmission system, proposes a data preprocessing method, and solves the problem of difficult extraction of deterioration index in the multichannel strong noise signal of the hydraulic system through the information fusion algorithm and the mean smoothing algorithm. According to the abnormal data judgment criterion based on the sliding window, the identification of the early fault starting point of the hydraulic system is realized, which provides a basis for the start-up fault prediction and the division of normal and degraded states. A multi-channel fusion fault prediction algorithm for hydraulic systems based on a Long-Short Term Memory (LSTM) deep network is proposed, which realizes long-term and high-precision fault prediction of hydraulic systems. Finally, the oil failure prediction algorithm of the integrated transmission system is verified by using real vehicle fault data.