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.
Highlights
The vibration and strain signals of a machine are mostly applied to provide dynamic information of the machine’s working condition [1,2], even though they have some common disadvantages, such as contact measurement, limited detecting positions and difficult to maintain detectors in some severe situations
The adaptive threshold denoising method based on WTD-IFOA can be summarized as follows: Step 2.1: For the sake of convenient calculation, the sound signal is first quantized into a certain range
9 of denoising performance of standard soft threshold denoising method (SST), a threshold function‐based noise elimination solution proposed in [10] (TFB), wavelet threshold denoising noise elimination solution proposed in [10] (TFB), wavelet threshold denoisingoptimized optimizedby byfruit genetic optimized by genetic algorithm (WTD‐GA), wavelet threshold denoising fly algorithm (WTD-GA), wavelet threshold denoising optimized by fruit fly optimization algorithm optimization algorithm (WTD‐FOA) and the proposed WTD‐IFOA were compared subsequently
Summary
The vibration and strain signals of a machine are mostly applied to provide dynamic information of the machine’s working condition [1,2], even though they have some common disadvantages, such as contact measurement, limited detecting positions and difficult to maintain detectors in some severe situations. One of the most important preconditions for ABS is eliminating noise from the initial sound signal, and the performance of denoising directly influences the effect of subsequent processing [5,6]. Fruit fly optimization algorithm (FOA) was proposed by Pan in 2012 [16,17,18]. Bearing the above observations in mind, an adaptive wavelet threshold denoising method for machinery sound based on an improved FOA (WTD-IFOA) is proposed.
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