Browse Publications Technical Papers 2010-36-0543

Noise Source Identification Using Phase Analysis 2010-36-0543

Noise source identification has been a subject well studied in the past few years. Automobile manufactures along with specialized supplies have been developed some methods in this matter. The importance of such subject is quite obvious, especially in the auto industry: identify potential problems and point out solutions for NVH.
There are several methods of noise source identification widely used. Among them, one can mention "Hotspot Search," which consists of noise intensity measurement, mapping and ranking the relative contribution of each substructure of one body. Another method used, one can point out is the STSF (Spatial Transformation of Sound Fields). It consists of a measurement over a scan plane using a set of microphone array. In this way, a 2D sound field can be transformed in a 3D description and source direction can be identified.
However, when noise and vibration sources need to be identified together in a multi-body system, conventional techniques might not be the best choice. In this scenario, a signal phase analysis was developed in order to cope with a low frequency noise identified in a vehicle. The noise occurrence was measured and identified in terms of frequency. The measurement setup was described using accelerometers and microphones installed in the vehicle suspension.
The vibration path was determined using bode diagrams, as long with the noise source. The delay among signal is easy to obtain and gives a clear picture of the vibration/noise phenomena. The condition in which the vibration was excited was also possible to be identified through the comparison of bode diagrams during the noise. The method helped to identify a resonance frequency of the brake caliper coupled with brake pads, generating a self-excited non-linear phenomenon.
Phase analysis method has shown to be suitable not only for noise but also for vibration. As it is simple to be applied and also competitive cost wise in comparison with other methods, it represents a powerful tool in noise analysis.


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