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The data measured during an ISO 362 pass-by-noise test are strongly non-stationary due to the fast acceleration of the vehicle and its moving position with respect to the ISO microphone position. Nevertheless, one would like to obtain an understanding of the relative contribution of the various noise generating components during the test. Since the classical signal analysis procedures based on the FFT calculation and auto/crosspower averaging for coherence/correlation analysis are no longer applicable, as they implicitly assume signal (and process) stationarity, an approach based on Autoregressive Vector (ARV) modelling of a set of measurement signals was developed and applied. An ARV model is calculated directly from a set of time data of limited duration.

This paper discusses the use of principal response analysis to earn insight in the operating behaviour of a combine harvester, causing objectionably high noise levels in the operator's cabin. Principal response analysis is able to extract more information from a set of operating response spectra, making use of multiple reference signals, by entering the concept of dimensionality of operating (acoustic and deflection) field shapes. The proposed method is demonstrated to offer a generic insight into the dimensionality of the problem (number of noise sources) and in the existence of possible noise transmission paths (air-borne as well as structure-borne) to the cabin.