Transactions of the Korean Society of Machine Tool Engineers (한국공작기계학회논문집)

The Korean Society of Manufacturing Technology Engineers (한국생산제조학회)
권호 표지

Diagnosis of a Pump by Frequency Analysis of Operation Sound

펌프의 작동음 주파수 분석에 의한 진단

  • Published : 2004.10.01
Download PDF
⟨ Previous Next ⟩

Abstract

A fundamental study for developing a system of fault diagnosis of a pump is performed by using neural network. The acoustic signals were obtained and converted to frequency domain for normal products and artificially deformed products. The signals were obtained in various driving frequencies in order to obtain many types of data from a limited number of pumps. The acoustic data in frequency domain were managed to multiples of real driving frequency with the aim of easy comparison. The neural network model used in this study was 3-layer type composed of input, hidden, and output layer. The normalized amplitudes at the multiples of real driving frequency were chosen as units of input layer. Various sets of teach signals made from original data by eliminating some random cases were used in the training. The average errors were approximately proportional to the number of untaught data. The results showed neural network trained by acoustic signals can be used as a simple method for a detection of machine malfuction or fault diagnosis.

Keywords

References

  1. Asakura, T., Kobayashi, T., Xu, B., and Hayashi, S., 2000, 'Fault Diagnosis System for Machines Using Neural Networks, 'JSME International Journal, Series C, Vol. 43, pp. 364- 371
  2. Staroswiecki, M., 2000, 'Quantitative and Qualitative Models for Fault Detection and Isolation,' Mechanical Systems and Signal Processing, Vol. 14, pp. 301-325 https://doi.org/10.1006/mssp.2000.1293
  3. Danai, K., and Chin, H., 1991, 'Fault Diagnosis with Process Uncertainty,' Journal ofDynamic Systems, Measurement and Control, Vol. 113, pp. 339-343 https://doi.org/10.1115/1.2896416
  4. Staszewski, W., 1998, 'Wavelet Based Compression and Feature Selection for Vibration Analysis,' Journal of Sound and Vibration, Vol. 211, pp. 735-760 https://doi.org/10.1006/jsvi.1997.1380
  5. Lin, J., and Qu, L., 2000, 'Feature Extraction Based on Morlet Wavelet and Its Application for Mechanical Fault Diagnosis,' Journal of Sound and Vibration, Vol. 234, pp. 135-148 https://doi.org/10.1006/jsvi.2000.2864
  6. Zang, C., and Imregun, M., 2001, 'Structural Damage Detection Using Artificial Neural Networks and Measured FRF Data Reduced via Principal Component Projection,' Journal of Sound and Vibration, Vol. 242, pp. 813-827 https://doi.org/10.1006/jsvi.2000.3390
  7. Chen, Z., and Mechefske, C., 2002, 'Diagnosis of Machinery Fault Status using Transient Vibration Signal Parameters,' Journal of Vibration and Control, Vol. 8, pp. 321-335 https://doi.org/10.1177/107754602023686
  8. Jung, W. S., 2000, Fault Diagnosis of a Pump Using Analysis of Vibration Signal(in Korean), Graduate School of Kunsan National University, Korea
  9. Zimmerman, D. C., Smith, S. W., Kim, H. M., and Bartkowicz, 1996, 'An Experimental Study of Structural Health Monitoring Using Incomplete Measurements,' ASME Journal of Vibration and Acoustics, Vol. 118, pp. 543-550 https://doi.org/10.1115/1.2888333
  10. Bae, Y. H. and Lee, S. K., 1998, 'Multiple Fault Diagnosis Method by Modular Artificial Neural Network,' Journal of KSPE, Vol. 15, No.2, pp. 35-44
  11. Duffey, T. A., Doebling, S. W., Farrar, C. R., Baker, W. E., and Rhee, W. H., 2001, 'Vibration-Based Damage Identification in Structures Exhibiting Axial and Torsional Response,' ASME Journal of Vibration and Acoustics, Vol. 123, pp. 84-91 https://doi.org/10.1115/1.1320445
  12. Min, H. S., Lee, S. H., and Ahn, S. E., 2002, 'Monitoring of Laser Fusion Cutting Using Acoustic Emission,' Trans. of KSMTE, Vol, 11, No.3, pp. 39-44
  13. Stech, D. J., 1994, 'Towards Real-time Continuous System Identification Using Modified Hopfield Neural Networks,' Proceedings of 12th lMAC.(International Modal Analysis Conference), pp. 1135-1140
  14. Kirkegaard, P. H. and Rytter, A., 1994, 'Use of Neural Networks for Damage Assessment in a Steel Mast,' Proceedings of 12th lMAC., pp. 1128-1134
  15. Kim, D. H., 2003, 'A Study on Friction Coefficient Prediction of Hydraulic Driving Members by Neural Network,' Trans. of KSMTE, Vol. 12, No. 5, pp. 53-58