Tool-Emitted Sound Signal Decomposition Using Wavelet and Empirical Mode Decomposition Techniques—A Comparison

Abstract

Analysis of non-stationary and nonlinear sound signals obtained from dynamical processes is one of the greatest challenges in signal processing. Turning machine operation is a highly dynamic process influenced by many events, such as dynamical responses, chip formations and the operational conditions of machining. Traditional and widely used fast Fourier transformation and spectrogram are not suitable for processing sound signals acquired from dynamical systems as their results have significant deficiencies because of stationary assumptions and having an a priori basis. A relatively new technique, discrete wavelet transform (DWT), which uses Wavelet decomposition (WD), and the recently developed technique, Hilbert–Huang Transform (HHT), which uses empirical mode decomposition (EMD), have notably better properties in the analysis of nonlinear and non-stationary sound signals. The EMD process helps the HHT to locate the signal’s instantaneous frequencies by forming symmetrical envelopes on the signal. The objective of this paper is to present a comparative study on the decomposition of multi-component sound signals using EMD and WD to highlight the suitability of HHT to analyze tool-emitted sound signals received from turning processes. The methodology used to achieve the objective is recording a tool-emitted sound signal by way of conducting an experiment on a turning machine and comparing the results of decomposing the signal by WD and EMD techniques. Apart from the short mathematical and theoretical foundations of the transformations, this paper demonstrates their decomposition strength using an experimental case study of tool flank wear monitoring in turning. It also concludes HHT is more suitable than DWT to analyze tool-emitted sound signals received from turning processes.