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Appl. Sci. 2017, 7(3), 215; doi:10.3390/app7030215

A Novel Denoising Method for an Acoustic-Based System through Empirical Mode Decomposition and an Improved Fruit Fly Optimization Algorithm

1
School of Mechatronic Engineering, China University of Mining & Technology, No. 1 Daxue Road, Xuzhou 221116, China
2
Institute of Sound and Vibration Research, University of Southampton, Highfield, Southampton SO17 1BJ, UK
*
Author to whom correspondence should be addressed.
Academic Editor: César M. A. Vasques
Received: 5 January 2017 / Revised: 10 February 2017 / Accepted: 17 February 2017 / Published: 23 February 2017
(This article belongs to the Section Acoustics)
View Full-Text   |   Download PDF [6250 KB, uploaded 23 February 2017]   |  

Abstract

Generally, the sound signal produced by transmission unit or cutting unit contains abundant information about the working state of a machine. The acoustic-based diagnosis system presents some distinct advantages in some severe conditions particularly due to its unique non-contact measurement and unlimited use at the installation site. However, the original acoustic signal collected from manufacture process is always polluted by various background noises. In order to eliminate noise components from machinery sound effectively, an empirical mode decomposition (EMD) threshold denoising method optimized by an improved fruit fly optimization algorithm (IFOA) is launched in this paper. The acoustic signal was first decomposed by the adaptive EMD to obtain a series of intrinsic mode functions (IMFs). Then, the soft threshold function was applied to shrink the IMF coefficients. While the threshold of each IMF was determined by statistical estimation and empirical value for traditional EMD denoising, the denoising effect was often not desired and time-consuming. To solve these disadvantages, fruit fly optimization algorithm (FOA) was introduced to search global optimal threshold of each IMF. Moreover, to enhance the group diversity during production of the next generation of fruit flies and balance the local and global searching ability, a variation coefficient and a disturbance coefficient was introduced to the basic FOA. Then, a piece of simulated acoustic signal produced by the train was applied to validate the proposed EMD and IFOA threshold denoising (EMD-IFOA). The simulation results, which decreased 35.40% and 18.92% in mean squared error (MSE) and percent root mean square difference (PRD) respectively, and increased 40.36% in signal-to-noise ratio improvement (SNRimp) compared with basic EMD denoising scheme at SNR = 5 dB, illustrated the effectiveness and superiority of the proposed approach. Finally, the proposed EMD-IFOA was conducted on an actual acoustic-based diagnosis system for cutting state recognition of the coal mining shearer to demonstrate the practical effect. View Full-Text
Keywords: threshold denoising; acoustic signal; empirical mode decomposition; improved fruit fly optimization algorithm; variation coefficient; disturbance coefficient; wavelet transform threshold denoising; acoustic signal; empirical mode decomposition; improved fruit fly optimization algorithm; variation coefficient; disturbance coefficient; wavelet transform
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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.; Liu, X. A Novel Denoising Method for an Acoustic-Based System through Empirical Mode Decomposition and an Improved Fruit Fly Optimization Algorithm. Appl. Sci. 2017, 7, 215.

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