Application of Short Time Fourier Transform and Wavelet Transform for Sound Source Localization Using Single Moving Microphone in Machine Condition Monitoring


The paper discusses means to predict sound source position emitted by fault machine components based on a single microphone moving in a linear track with constant speed. The position of sound source that consists of some frequency spectrum is detected by time-frequency distribution of the sound signal through Short Time Fourier Transform (STFT) and Continues Wavelet Transform (CWT). As the amplitude of sound pressure increases when the microphone moves closer, the source position and frequency are predicted from the peaks of time-frequency contour map. Firstly, numerical simulation is conducted using two sound sources that generate four different frequencies of sound. The second case is experimental analysis using rotating machine being monitored with unbalanced, misalignment and bearing defect. The result shows that application of both STFT and CWT are able to detect multiple sound sources position with multiple frequency peaks caused by machine fault. The STFT can indicate the frequency very clearly, but not for the peak position. On the other hand, the CWT is able to predict the position of sound at low frequency very clearly. However, it is failed to detect the exact frequency because of overlapping.

[1] J. Lanslots, F. Deblawe, and K. Janssens, Selecting Sound Source Localization Techniques for Industrial Applications, Sound Vibrat, 6–9, (2010).

[2] C. Faller and J. Merimaa, Source localization in complex listening situations: selection of binaural cues based on interaural coherence, J Acoust Soc Am, 116, 3075–3089, (2004).

[3] K. Shibata, A. Takahashi, and T. Shirai, Fault diagnosis of rotating machinery through visualization of sound signal, J Mech Syst Signal Process, 14, 229–241, (2000).

[4] P. W. Tse, W. Yang, and H. Y. Tam, Machine fault diagnosis through an effective exact wavelet analysis, J Sound Vibrat, 277, 1005–1024, (2004).

[5] H. Bendjama, S. Bouhouche, and M. Boucherit, Application of Wavelet Transform for Fault Diagnosis in Rotating Machinery, International Journal of Machine Learning and Computing, 2, no. 1, 82–87, (2012).

[6] P. Aarabi and S. Mavandadi, Robust sound localization using conditional time– frequency histograms, Inf Fusion, 4, 111–122, (2003).

[7] J. Cech, R. Mittal, A. Deleforge, and J. Sanchez-Riera, Active-Speaker Detection and Localization with Microphones and Cameras Embedded into a Robotic Head (2013), IEEE-RAS International Conference on Humanoid Robots, Atlanta, United States, Oct 2013.

[8] O. M. Bouzid, G. Tian, J. Neasham, and B. Sharif, Envelope and Wavelet Transform for Sound Localization at Low Sampling Rates in Wireless Sensor Networks, Journal of Sensors, 2012, (2012).

[9] B. Ginn, J. Gomes, and J. Hald, Recent advances in Rail Vehicle Moving Source Beamforming, Internoise, 15-18 September 2013, Innsbrug-Austria.