A spectral self-focusing fault diagnosis method for automotive transmissions under gear-shifting conditions

Abstract

Transmissions are core components of automobiles in adjusting the speed. The time-varying operation conditions, particularly the frequent gear shifting, present significant challenges for condition monitoring and fault locating of transmissions. At present, most existing studies primarily focus on the issues arising from time-varying speed, but they do not consider the challenges introduced by gear shifting. Gear shifting changes the meshing gear pairs within the transmission, causing the monitoring indicator amplitude fluctuates greatly. Consequently, the indicator fails to reflect the overall degradation trend of the transmission. To address this problem, this paper proposes a fault diagnosis method for automotive transmissions with the consideration of gear shifting. Our contributions are as follows: Firstly, we propose a spectral variation sparsity indicator (SVSI) based on the order spectrum at each gear position. Secondly, we fuse SVSI from different gear positions and create a comprehensive indicator called weighted health indicator (WHI). Finally, a diagnosis method based on SVSI and WHI is proposed for automotive transmissions under gear-shifting conditions. The effectiveness of the proposed method is validated using datasets from four automotive transmissions. The results demonstrate that our method is capable of detecting faults prior to inspection and accurately identifying faulty gears. Moreover, the performance of WHI is compared with several existing indicators. The results demonstrate that WHI exhibits stronger correlation, monotonicity, and robustness. This indicates that WHI is able to effectively mitigate the influence of gear shifting, thereby better reflecting the overall degradation trend of the transmission. Consequently, our proposed method significantly contributes to the field of condition monitoring and fault locating for automotive transmissions.