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

Meifal Rusli

doi:10.18502/keg.v1i1.488

Abstract

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.

Highlights

Sound source localization is a complex work that acoustic engineers face today
The sound position and frequency peaks are be detected by the peak of the time-frequency distribution of the sound signal using short time Fourier transform (STFT) and continues wavelet transform (CWT)
One approach is short time Fourier transform (STFT) by splitting an acoustic signal into segments in time domain by proper selection of a window function and to carry out a Fourier transform on each of these segments separately and to deliver an instantaneous spectrum. Another approach is the continuous wavelet transform (CWT), where the non-stationary acoustic signal to be analyzed is filtered into different frequency bands, which are split into segments in time domain and their frequency contents and energy are analyzed

Summary

Introduction

Sound source localization is a complex work that acoustic engineers face today. Some standards based on microphone arrays are used to analyze the noise source. The sound position and frequency peaks are be detected by the peak of the time-frequency distribution of the sound signal using short time Fourier transform (STFT) and continues wavelet transform (CWT). Each defect generates specific frequency of vibration and sound This single moving microphone method has benefit that could be developed for autonomous or robotic condition monitoring system with simpler and cheaper devices and analysis rather than multi-channel microphones array. One approach is short time Fourier transform (STFT) by splitting an acoustic signal into segments in time domain by proper selection of a window function and to carry out a Fourier transform on each of these segments separately and to deliver an instantaneous spectrum Another approach is the continuous wavelet transform (CWT), where the non-stationary acoustic signal to be analyzed is filtered into different frequency bands, which are split into segments in time domain and their frequency contents and energy are analyzed. Time-frequency distribution of the sound signal is detected as the sound source position in each frequency

Numerical Simulation

The Rotor Dynamics Model

Conclusion