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http://dx.doi.org/10.9717/kmms.2018.21.8.837

Sound Monitoring System of Machining using the Statistical Features of Frequency Domain and Artificial Neural Network  

Lee, Kyeong-Min (Dept. of IT Convergence and Application Engineering, Pukyong National University)
Vununu, Caleb (Dept. of IT Convergence and Application Engineering, Pukyong National University)
Lee, Suk-Hwan (Dept. of Information Security, Tongmyong University)
Kwon, Ki-Ryong (Dept. of IT Convergence and Application Engineering, Pukyong National University)
Publication Information
Journal of Korea Multimedia Society / v.21, no.8, 2018 , pp. 837-848 More about this Journal
Abstract
Monitoring technology of machining has a long history since unmanned machining was introduced. Despite the long history, many researchers have presented new approaches continuously in this area. Sound based machine fault diagnosis is the process consisting of detecting automatically the damages that affect the machines by analyzing the sounds they produce during their operating time. The collected sound is corrupted by the surrounding work environment. Therefore, the most important part of the diagnosis is to find hidden elements inside the data that can represent the error pattern. This paper presents a feature extraction methodology that combines various digital signal processing and pattern recognition methods for the analysis of the sounds produced by tools. The magnitude spectrum of the sound is extracted using the Fourier analysis and the band-pass filter is applied to further characterize the data. Statistical functions are also used as input to the nonlinear classifier for the final response. The results prove that the proposed feature extraction method accurately captures the hidden patterns of the sound generated by the tool, unlike the conventional features. Therefore, it is shown that the proposed method can be applied to a sound based automatic diagnosis system.
Keywords
Machining; Band-pass Filter; Magnitude Spectrum; Fourier Analysis; Statistical Functions; Machine Fault Diagnosis;
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Times Cited By KSCI : 2  (Citation Analysis)
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