Version 1
: Received: 3 June 2024 / Approved: 3 June 2024 / Online: 4 June 2024 (08:19:15 CEST)
How to cite:
Joseph, E. R.; J, H.; Thangavel, B.; Nor, A.; Lim, T. L.; Mariathangam, P. R. Tool-Emitted Sound Signal Decomposition Using Wavelet and Empirical Mode Decomposition Techniques—A Comparison. Preprints2024, 2024060118. https://doi.org/10.20944/preprints202406.0118.v1
Joseph, E. R.; J, H.; Thangavel, B.; Nor, A.; Lim, T. L.; Mariathangam, P. R. Tool-Emitted Sound Signal Decomposition Using Wavelet and Empirical Mode Decomposition Techniques—A Comparison. Preprints 2024, 2024060118. https://doi.org/10.20944/preprints202406.0118.v1
Joseph, E. R.; J, H.; Thangavel, B.; Nor, A.; Lim, T. L.; Mariathangam, P. R. Tool-Emitted Sound Signal Decomposition Using Wavelet and Empirical Mode Decomposition Techniques—A Comparison. Preprints2024, 2024060118. https://doi.org/10.20944/preprints202406.0118.v1
APA Style
Joseph, E. R., J, H., Thangavel, B., Nor, A., Lim, T. L., & Mariathangam, P. R. (2024). Tool-Emitted Sound Signal Decomposition Using Wavelet and Empirical Mode Decomposition Techniques—A Comparison. Preprints. https://doi.org/10.20944/preprints202406.0118.v1
Chicago/Turabian Style
Joseph, E. R., Thong Leng Lim and Pushpa Rani Mariathangam. 2024 "Tool-Emitted Sound Signal Decomposition Using Wavelet and Empirical Mode Decomposition Techniques—A Comparison" Preprints. https://doi.org/10.20944/preprints202406.0118.v1
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 persuaded by many events such as dynamical responses, chip formations and operational conditions of machining. Traditional and widely used fast Fourier transformation and spectrogram is not suitable for processing sound signals acquired from dynamical systems as its result have significant deficiencies because of stationary assumptions and having priori basis. The relatively new technique, Discrete Wavelet Transform, which uses Wavelet decomposition and the recently developed technique, Hilbert Huang Transform (HHT) which uses Empirical Mode Decomposition have notably better properties in the analysis of nonlinear and non-stationary sound signals. This paper presents a comparative study on the decomposition of multicomponent sound signals using Empirical Mode Decomposition and Wavelet Decomposition to highlight the suitability found in HHT to analyze tool-emitted sound signals received from turning processes. 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.
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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