System Level Noise Source Identification and Diagnostics on a Vehicle Door Module

Noise problems are often system issues rather than component issues. Component manufacturers have been putting continued efforts into constantly improving the quality of their products. There are numerous tests and standards to assess the vibro-acoustic performance of individual components. But once all components are put together, the system response might be entirely different from those of individual components. Typical system level testing has primarily been used to identify bad assembled products from good ones. These tests are usually done as part of a quality control process and slow down production. Such tests usually provide little information about the root causes of noise and vibration problems and no insight into improving engineering designs for noise abatement.

This paper presents a new way of conducting system level noise diagnoses by using the Helmholtz Equation Least Squares (HELS) based Nearfield Acoustical Holography (NAH) technology [1]. This approach allows for reconstruction of all acoustic quantities, including the acoustic pressure, particle velocity, and acoustic intensity, and creating 3D acoustic images produced by an arbitrary source based on the acoustic pressures measured on a hologram surface at very close range to the source. It enables one to establish a direct correlation between sound and vibration. The current study involves a noise diagnostic test on a vehicle door assembly to understand system level interaction of the motor and the door module to compliment continued efforts to refine the motor to manufacture quieter assemblies. To identify noise sources, a conformal microphone array, covering the entire door surface was used to measure the acoustic pressures. This data was used to reconstruct the acoustic pressure, normal velocity, and acoustic intensity on the door module surface. In particular, the acoustic intensity and normal surface velocity were analyzed to identify the noise sources and understand the noise generation mechanisms.