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Appl. Sci. 2016, 6(7), 199; doi:10.3390/app6070199

Adaptive Wavelet Threshold Denoising Method for Machinery Sound Based on Improved Fruit Fly Optimization Algorithm

1
School of Mechatronic Engineering, China University of Mining and Technology, No.1 Daxue Road, Xuzhou 221116, China
2
School of Information and Electrical Engineering, China University of Mining and Technology, No.1 Daxue Road, Xuzhou 221116, China
3
Institute for Neural Computation, University of California, San Diego (UCSD), No.3950 Mahaila Ave, San Diego, CA 92093, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Gino Iannace
Received: 5 May 2016 / Revised: 27 June 2016 / Accepted: 1 July 2016 / Published: 6 July 2016
(This article belongs to the Special Issue Audio Signal Processing)
View Full-Text   |   Download PDF [8755 KB, uploaded 6 July 2016]   |  

Abstract

As the sound signal of a machine contains abundant information and is easy to measure, acoustic-based monitoring or diagnosis systems exhibit obvious superiority, especially in some extreme conditions. However, the sound directly collected from industrial field is always polluted. In order to eliminate noise components from machinery sound, a wavelet threshold denoising method optimized by an improved fruit fly optimization algorithm (WTD-IFOA) is proposed in this paper. The sound is firstly decomposed by wavelet transform (WT) to obtain coefficients of each level. As the wavelet threshold functions proposed by Donoho were discontinuous, many modified functions with continuous first and second order derivative were presented to realize adaptively denoising. However, the function-based denoising process is time-consuming and it is difficult to find optimal thresholds. To overcome these problems, fruit fly optimization algorithm (FOA) was introduced to the process. Moreover, to avoid falling into local extremes, an improved fly distance range obeying normal distribution was proposed on the basis of original FOA. Then, sound signal of a motor was recorded in a soundproof laboratory, and Gauss white noise was added into the signal. The simulation results illustrated the effectiveness and superiority of the proposed approach by a comprehensive comparison among five typical methods. Finally, an industrial application on a shearer in coal mining working face was performed to demonstrate the practical effect. View Full-Text
Keywords: wavelet threshold denoising; sound signal; wavelet transform; improved fruit fly optimization algorithm; fly distance range wavelet threshold denoising; sound signal; wavelet transform; improved fruit fly optimization algorithm; fly distance range
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. (CC BY 4.0).

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MDPI and ACS Style

Xu, J.; Wang, Z.; Tan, C.; Si, L.; Zhang, L.; Liu, X. Adaptive Wavelet Threshold Denoising Method for Machinery Sound Based on Improved Fruit Fly Optimization Algorithm. Appl. Sci. 2016, 6, 199.

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