Comparison of Support Vector Machine and Radial Basis Artificial Neural Network in Fault Diagnosis Of Bearings Using Short Time Fourier and Choi-Williams Transforms

سال انتشار: 1394
نوع سند: مقاله کنفرانسی
زبان: انگلیسی
مشاهده: 564

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شناسه ملی سند علمی:

NRIME01_052

تاریخ نمایه سازی: 27 بهمن 1394

چکیده مقاله:

This paper focus on the fault diagnosis of bearing with two soft computing methods. Firstly, the vibration signal of a test rig is done. An intelligent method is selected to diagnose a ball bearing type 1206, using the vibration signal. This paper presents two methods time-frequency based approach for classifying the vibration signals of rolling bearing. It uses the features extracted from the time-frequency distribution (TFD) of the vibration signal segments. The results of applying the method to a database of real signals and then classifying feature by radial basis artificial neural network and support vector machine reveal that, for the given classification task, the selected features consistently exhibit a degree of discrimination between the vibration signals collected from healthy and fault machine. A comparison between the performances of the features extracted from two TFDs shows that the Choi-williams is better than STFT. Also the results show that the accuracy of SVM for classification is better than RBF artificial neural network.

نویسندگان

Mohammad Heidari

Mechanical Engineering Group, Abadan Branch, Islamic Azad University, Abadan, Iran

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  • Rafiee, J., Arvani, F., Harifi, A., Sadeghi, M.H. (2007). Intelligent ...
  • Samantha, B., Al-Balushi, K.R. (2003). Artificial neural networks based fault ...
  • Yang, Y., Dejie, Y., Cheng, J. (2007). A fault diagnosis ...
  • Yuan, S., Chu, F. (2007). Fault diagnosis based On support ...
  • Finley, W.R., Hodowance, M.M., Holter, W.G. (2007). An analytical approach ...
  • Lei, Y., He, Z., Zi, Y. (2009). Application of an ...
  • Saravanan, N., Kumar, V.N.S., Ramac handran, K.I. (2008). A comparative ...
  • Xian, G., Zeng, B. (2009). An intelligent fault diagnosis method ...
  • Paya, B.A., Esat, I.I., Badi, M.N.M. (1997). Artificial neural networks ...
  • Abbasion, S., Rafsanjani, A., Farshidianfar, A., Irani, N. (2007). Rolling ...
  • Prabhakar, S., Mohanty, A.R., Sekhar, A.S. (2002). Application of discrete ...
  • Gao, X.Z., Ovaska, S.J. (2001). Soft computing methods in motor ...
  • Yan, R. (2007). Base wavelet selection criteria for non-stationary vibration ...
  • Al-Badour, F., Cheded, L, Sunar, M. (2010). Non-stationary vibration signal ...
  • Antonino -Daviu, J.A., C _ imente -Alarcon, V., Pons-Llinares, P., ...
  • Boashash, B., (2003). Time Frequency Signal Analysis and Processing: A ...
  • Kaptan, T., (2009). Adj ustable-Speed Drive Bearing-Fault Detection ViaWavelet Packet ...
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