The use of intrinsic mode functions to characterize shock and vibration in the distribution environment

This paper describes an innovative approach, based on the instrinsic mode functions (IMFs), to characterize the nature of mechanical vibration encountered in transport vehicles. The paper highlights the importance of understanding the nature of transport vibration and shows that their accurate characterization is essential for the optimization of protective packaging. Although there have been numerous studies aimed at characterizing random vibration during transport, the majority of those have been limited to applying relatively conventional signal analysis techniques, such as the average power spectral density (PSD). This paper investigates the benefits offered by the recently introduced Hilbert-Huang transform when characterizing non-stationary random vibration in comparison with more traditional Fourier analysis-based techniques. The paper describes the operation of the Hilbert-Huang transform, which was developed to assist in the analysis of non-Gaussian and non-stationary random data. The Hilbert-Huang transform is based on the empirical mode decomposition (EMD) technique used to produce a finite number of IMFs, which, as a set, provide a complete description of the process. It is shown how these IMFs are well suited to the application of the Hilbert-Huang transform to determine the magnitude and instantaneous frequency of each IMF. The technique is applied to various records of random vibration data collected from transport vehicles in order to illustrate the benefits of the method in characterizing the nature of non-stationarities present in transport vibration.