Robust Fault Diagnosis of Electric Vehicle Drivetrain Using Amplitude Adjustment Techniques

Abstract

Most electric vehicle drivetrain fault diagnosis methods have been validated only under constant load and rotational speed conditions, showing limited performance in real driving environments where load and speed continuously vary. This study proposes a novel vibration signal normalization method that combines order tracking with physics-based amplitude adjustment techniques to improve diagnostic accuracy under variable operating conditions. Order tracking addresses the problem of frequency variation of vibration signals that vary with speed over time. The proposed method converts vibration signals under variable speed conditions into pseudo-stationary signals of equivalent levels by adjusting amplitudes through factors that consider both centrifugal and tangential forces acting on rotating components in the drivetrain. To validate the effectiveness of the proposed technique, experiments were conducted using actual electric vehicles equipped with drivetrains at various degradation levels. Drivetrain vibration data were collected and evaluated across multiple operating scenarios. Experimental results demonstrate that the proposed method reduces variability across different speed conditions compared to raw signals. The proposed method shows promise for robust drivetrain diagnosis applications even under variable speed conditions, addressing a significant limitation of existing diagnostic approaches.