Diagnosing and Reducing Noise and Vibration in Automotive DC Motors with Time Synchronous Averaging

This study explores the application of Time Synchronous Averaging (TSA) for diagnosing Noise, Vibration, and Harshness (NVH) characteristics in an automotive sunroof assembly powered by a Direct Current (DC) motor. TSA is an advanced signal-processing technique that isolates periodic signals from noise, enhancing fault detection. The research identifies rotor misalignment as the dominant cause of excessive NVH. To address this, an experimental modification to the motor’s end washer was implemented, resulting in a significant reduction in vibration amplitudes at upper harmonics. These findings demonstrate TSA’s effectiveness in optimizing NVH characteristics for smaller rotating systems and demonstrate an effective approach for optimizing NVH performance in small-scale rotating systems, particularly in automotive applications.