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The increasing complexity and application of NVH simulation, test and measurement tasks in the automotive industry have led to ever increasing amounts of numerical data and data management systems. Many times, simulation and testing systems only provide proprietary database structures for the managing the information they generate. This has led to a plethora of incompatible data formats and databases. Furthermore, vendor-proprietary databases sometimes make it difficult or impossible for the owner of the data to gain free, direct access to the stored information!

Squeak and rattle (S&R) noise is an important issue in the automobile industry because the presence of audible S&R in a vehicle can convey the impression of poor quality to the customer. Furthermore, addressing S&R problems can be a significant warranty cost issue. Overall-all level types of noise assessment, such as dB SPL and loudness, are not always suitable measures for S&R detection, characterization and scaling. This is primarily due to the highly dynamic temporal and localized spectral characteristics of most S&R events. In this report, Fourier and filterbank methods for the analysis of S&R events are considered, and several criteria for the detection and scaling of S&R noise are examined using data measured both in an ultra-quiet laboratory situation and in several realistic, on-road driving conditions. Recommendations are made for an analysis method that is robust across both laboratory and on-road measurement conditions.