Acoustic Signal based Non-Contact Ball Bearing Fault Diagnosis Using Adaptive Wavelet De-Noising



Published Sep 4, 2023
Wonho Jung Sung-Hyun Yun Yong-Hwa Park


This paper presents a non-contact fault diagnostic method for ball bearing using adaptive wavelet denoising, statistical-spectral acoustic features, and one-dimensional (1D) convolutional neural networks (CNN). The health conditions of the ball bearing are monitored by microphone under noisy condition. To eliminate noise, adaptive wavelet denoising method based on kurtosis-entropy (KE) index is proposed. Multiple acoustic features are extracted base on expert knowledge. The 1D ResNet is used to classify the health conditions of the bearings. Case study is presented to examine the proposed method’s capability to monitor the condition of ball bearings. The fault diagnosis results were compared with and without the adaptive wavelet denoising. The results show its effectiveness of the proposed fault diagnostic method using acoustic signals.  

Abstract 69 | PDF Downloads 71



Acoustic Signals, Adaptive Wavelet Denoising, Fault Diagnosis

Bruant, I., Gallimard, L., & Nikoukar, S., (2010). Optimal piezoelectric actuator and sensor location for active vibration control, using genetic algorithm. Journal of Sound and Vibration, vol. 329, pp. 1615-1635.

Cheng, J., Yang, Y., & Yu, D., (2010). The envelope order spectrum based on generalized demodulation time– frequency analysis and its application to gear fault diagnosis. Mechanical Systems and Signal Processing, vol. 24, pp. 508-521.

Antoni, J., (2007). Fast computation of the kurtogram for the detection of transient faults. Mechanical Systems and Signal Processing, vol. 21, pp. 108-124.

Antoni, J., & Randall, R. B., (2006). The spectral kurtosis: application to the vibratory surveillance and diagnostics of rotating machines. Mechanical Systems and Signal Processing, vol. 20, pp. 308-331.

Yang, W. X., & Tse, P. W., (2003). Development of an advanced noise reduction method for vibration analysis based on singular value decomposition. NDT & E International, vol. 36, pp. 419-432.

Yan, R., Gao, R. X., & Chen, X., (2014). Wavelets for fault diagnosis of rotary machines: A review with applications. Signal Processing, vol. 96, pp. 1-15.

Qiu, H., Lee, J., Lin, J., & Yu, G., (2006). Wavelet Filter- based Weak Signature Detection Method and Its Application on Rolling Element Bearing Prognostics. Journal of Sound and Vibration, vol. 289, pp. 1066- 1090.

Yuan, J., He, Z., & Zi, Y., (2010). Gear fault detection using customized multiwavelet lifting schemes. Mechanical Systems and Signal Processing, vol. 24, pp. 1509-1528.

Zhen, L., Zhengjia, H., Yanyang, Z., & Yanxue, W., (2008). Customized wavelet denoising using intra- and inter- scale dependency for bearing fault detection. Journal of Sound and Vibration, vol. 313, pp. 342-359.

Westfall, P. H., (2014). Kurtosis as Peakedness, 1905 - 2014. R.I.P. The American statistician, vol. 68, pp. 191-195.

Huo, Z., Martinez-Garcia, M., Zhang, Y., Yan, R., & Shu, L., (2020). Entropy Measures in Machine Fault Diagnosis: Insights and Applications. IEEE Transactions on Instrumentation and Measurement, vol. 69, pp. 2607-2620.
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