Skip to main content
SpringerLink
Log in

Time Frequency Image and Artificial Neural Network Based Classification of Impact Noise for Machine Fault Diagnosis

  • Regular Paper
  • Published:
International Journal of Precision Engineering and Manufacturing Aims and scope Submit manuscript

Abstract

This paper presents a method of classifying impact noises obtained from a washer machine by obtaining the time frequency image of the sound signals and applying an artificial neural network for classification. Classifying the impact noises is critical for fault detection and diagnosis of the machines, especially by distinguishing actual fault impact noises from background noises. Audio recordings are taken from a washing machine manufacturing assembly line where faults that commonly occur were measured. A short-time Fourier Transform is applied to obtain a time-frequency-image that is employed as the input signal to an artificial neural network (ANN) classifier. The ANN classifier distinguishes the different impact noises with 100% accuracy on the test data.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Kim, J. H., “Fault Detection for Manufacturing Home Air Conditioners Using Wavelet Transform,” International Journal of Precision Engineering and Manufacturing, vol. 17, no. 10, pp. 1299–1303, 2016.

    Article  Google Scholar 

  2. Ji, H. G. and Kim, J. H., “Fault Detection and Localization Using Wavelet Transform and Cross-Correlation of Audio Signal,” Journal of the Korean Society for Precision Engineering, vol. 31, no. 4, pp. 327–334, 2014.

    Article  Google Scholar 

  3. Taylor, J. I., “Identification of Bearing Defects by Spectral Analysis,” Journal of Mechanical Design, vol. 102, no. 2, pp. 199–204, 1980.

    Article  Google Scholar 

  4. Park, C.-S., Choi, Y.-C., and Kim, Y.-H., “Early Fault Detection in Automotive Ball Bearings Using the Minimum Variance Cepstrum,” Mechanical Systems and Signal Processing, vol. 38, no. 2, pp. 534–548, 2013.

    Article  Google Scholar 

  5. Zou, J., Chen, J., Pu, Y., and Zhong, P., “On the Wavelet Time-Frequency Analysis Algorithm in Identification of a Cracked Rotor,” The Journal of Strain Analysis for Engineering Design, vol. 37, no. 3, pp. 239–246, 2002.

    Article  Google Scholar 

  6. Kim, G., Baek, S., Hong, J.-W., Park, J., and Jhang, K.-Y., “Analysis of Transmitted Ultrasound Signals through Apples at Different Storage Times Using the Continuous Wavelet Transformation,” International Journal of Precision Engineering and Manufacturing, vol. 13, no. 11, pp. 1949–1954, 2012.

    Article  Google Scholar 

  7. Lee, J.-H. and Kim, D.-H., “Integrity Evaluation of Pipe Welding Zones Using Wavelet Transforms, and Specific Sensitivities Based on SH-EMAT Pulse-Echo Method,” International Journal of Precision Engineering and Manufacturing, vol. 15, no. 10, pp. 2051–2057, 2014.

    Article  Google Scholar 

  8. Cavaco, S. and Lewicki, M. S., “Statistical Modeling of Intrinsic Structures in Impacts Sounds,” The Journal of the Acoustical Society of America, vol. 121, no. 6, pp. 3558–3568, 2007.

    Article  Google Scholar 

  9. Gaver, W., “Using and Creating Auditory Icons,” Auditory Display-Sonification, Audification, and Auditory Interfaces, pp. 417–446, 1994.

    Google Scholar 

  10. Lee, Y.-J. and Lee, S.-K., “Classification of Noise Sources in a Printer and Its Application to the Development of Sound Quality Evaluation,” International Journal of Precision Engineering and Manufacturing, vol. 13, no. 4, pp. 491–499, 2012.

    Article  Google Scholar 

  11. Sugumaran, V. and Ramachandran, K., “Automatic Rule Learning Using Decision Tree for Fuzzy Classifier in Fault Diagnosis of Roller Bearing,” Mechanical Systems and Signal Processing, vol. 21, no. 5, pp. 2237–2247, 2007.

    Article  Google Scholar 

  12. Janssens, O., Slavkovikj, V., Vervisch, B., Stockman, K., Loccufier, M., et al., “Convolutional Neural Network Based Fault Detection for Rotating Machinery,” Journal of Sound and Vibration, vol. 377, pp. 331–345, 2016.

    Article  Google Scholar 

  13. Jia, F., Lei, Y., Lin, J., Zhou, X., and Lu, N., “Deep Neural Networks: A Promising Tool for Fault Characteristic Mining and Intelligent Diagnosis of Rotating Machinery with Massive Data,” Mechanical Systems and Signal Processing, vol. 72, pp. 303–315, 2016.

    Article  Google Scholar 

  14. Zeiler, M. D., Ranzato, M., Monga, R., Mao, M., Yang, K., et al., “On Rectified Linear Units for Speech Processing,” Proc. of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3517–3521, 2013.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mechatronics Engineering, Kyungsung University, 309 Suyoung-ro, Nam-gu, Busan, 48434, Republic of Korea

    Jung Hyun Kim

Authors
  1. Jung Hyun Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jung Hyun Kim.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kim, J.H. Time Frequency Image and Artificial Neural Network Based Classification of Impact Noise for Machine Fault Diagnosis. Int. J. Precis. Eng. Manuf. 19, 821–827 (2018). https://doi.org/10.1007/s12541-018-0098-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12541-018-0098-8

Keywords

Search

Navigation