The exploitation of wavelet de-noising to detect bearing faults

• Autorzy: Al-Raheem K. F. , ROY A. , Ramachandran K. P. , Harrison D. K. , Grainger S.

Warianty tytułu

PL

Wykorzystanie akustyki w identyfikacji uszkodzeń łożyska

• Języki publikacji: EN

Abstrakty

EN

Failure diagnosis is an important component of the Condition Based Maintenance (CBM) activities for most engineering systems. Rolling element bearings are the most common cause of rotating machinery failure. The existence of the amplitude modulation and noises in the faulty bearing vibration signal present challenges to effective fault detection method. The wavelet transform has been widely used in signal de-noising due to its extraordinary time-frequency representation capability. In this paper, we proposed new approach for bearing fault detection based on the autocorrelation of wavelet de-noised vibration signal through a wavelet base function derived from the bearing impulse response. To improve the fault detection process the wavelet parameters (damping factor and center frequency) are optimized using maximization kurtosis criteria to produce wavelet base function with high similarity with the impulses generated by bearing defects, that leads to increase the magnitude of the wavelet coefficients related to the fault impulses and enhance the fault detection process. The results show the effectiveness of the proposed technique to reveal the bearing fault impulses and its periodicity for both simulated and real rolling bearing vibration signals.

Słowa kluczowe

EN

kurtosis maximization; wavelet de-noising; autocorrelation; bearing fault detection; impulse-response wavelet safety; dependability; decision-making support; engineering

PL

bezpieczeństwo; niezawodność; systemy techniczne

• Wydawca: Wydawnictwo Instytutu Technicznego Wojsk Lotniczych
• Czasopismo: Journal of KONBiN
• Rocznik: 2007
• Tom: No. 1 (3)
• Strony: 7--16
• Opis fizyczny: Bibliogr. 12 poz.

Twórcy

autor

Al-Raheem K. F.

autor

ROY A.

autor

Ramachandran K. P.

autor

Harrison D. K.

autor

Grainger S.

• Deptartment of Mechanical and Industrial Eng., Caledonian College of Eng., Oman

Bibliografia

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• 2. Antoniadis I., Glossiotis G.: Cyclostationary analysis of rolling element bearing vibration signals.Journal of Sound and Vibration, v. 248, 5 (13), pp. 829-845, December 2001.
• 3. Li L., Qu L.: Cyclic statistics in rolling bearing diagnosis.Journal of Sound and Vibration, v. 267 (2), 16 October 2003.
• 4. Antoni J. Randall R. B.: Differential Diagnosis of gear and bearing faults.ASME Journal of Vibration and Acoustics, v. 124, pp. 165-171, 2002.
• 5. Purushotham V., Narayanan S., Prasad S. A.N: Multi-fault diagnosis of rolling bearing elements using wavelet analysis and hidden Markov model based fault recognition.NDT & E International, v. 38 (8), pp. 654-664, December 2005.
• 6. Shi D. F., Wang W. J., Qu L. S.: Defect detection for bearings using envelope spectra of wavelet transform.ASME, Journal of Vibration and Acoustics, v. 120, pp.567-574, October 2004.
• 7. Nikolaou N. G., Antoniadis I. A.: Demodulation of vibration signals generated by defects in rolling element bearings using complex shifted Morlet wavelets.Mechanical Systems and Signal Processing, v.16 (4), pp. 677-694, July 2002.
• 8. Junsheng C., Dejie Y., Yu Y.: Application of an impulse response wavelet to fault diagnosis of rolling bearings. Mechanical Systems and Signal Processing, In press, corrected.
• 9. Yang W. X., Tse P. W.: An advanced strategy for detecting impulses in mechanical signals.Transactions of the ASME, v.127, June 2005, pp.280-284.
• 10. W. X. Yang, and X. M. Ren, Detecting Impulses in mechanical signals by wavelets.EURASIP Journal on Applied Signal Processing, 8, pp. 1156-1162, 2004.
• 11. Qiu H., Lee J., Lin J., Yu G.: Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics.Journal of Sound and Vibration, v. 289 (4-5), pp. 1066-1090, 7 February 2006.
• 12. Lin J., Zuo M. J., Fyfe K. R.: Mechanical fault detection based on the wavelet de-noising technique.ASME J. of Vibration and Acoustics, v.126, pp. 9-16, January 2004.