Fault diagnostic system for rotating machine based on Wavelet packet transform and Elman neural network
An efficient fault diagnosis system is needed for industry because it can optimize the resources management and improve the performance of the system. In this study, a fault diagnostic system is proposed for rotating machine using wavelet packet transform (WPT) and elman neural network (ENN) techniques. In most fault diagnosis for mechanical systems, WPT is a well-known signal processing technique for fault detection and identification. In previous work, WPT can improve the continuous wavelet transform (CWT) used over a longer computing time and huge operand. It can also solve the frequency-band disagreement by discrete wavelet transform (DWT) only breaking up the approximation version. In the experimental work, the extracted features from the WPT are used as inputs in an Elman neural network. The results show that the scheme can reliably diagnose four different conditions and can be considered as an improvement of previous works in this field.
- F. Hlawatsch and G. F. Boudreaux-Bartels, "Linear and quadratic time frequency signal representations," lEEE Signal Processing Mag. vol. 9, pp. 21-67, Apr. 1992
- H. Zheng, Z, Li, & X, Chen, "Gear fault diagnosis based on continuous wavelet transfoml.", Mechanical Syslem and Signal Processing, vol. 16, pp. 447-457 2002
- E. Ortiz, & V. Syrmos, "Support vector machines and wavelet packet analysis for fau lt detection and identification", IJCNN 06. lnlernalional Joint Conference on Neural Networks, pp. 3449- 3456, 2006
- X. Z. Gao and S. J . Ovaska, "Genetic algorithm traning of Elman neural network in motor fault detection", Neural Compuling and Applicalions, vol. 11, no. 1, pp. 37-44, 2002
- C. K. Sung, H. M. Tai, & C. W. Chen, "Locating defects of a gear system by the technique of wavelet transform.", Mechanism and Machine Theory, vol. 35, pp. 1169-1182. 2000
- C. Li , Z. Song & P. Li, "Bearing fault detection via wavelet packet transform and rough set ttheory." Proceedings of Fifth World Congress on lnlelligenl Control and Aulomalion, vol. 2, pp. 1663-1666, 2004
- G. G. Yen & K. C. Lin, "Wavelet packet feature extraction for vibration monitoring", IEEE Transactions on Industrial Electronics, vol. 47, pp. 650-667, 2000
- http ://www.bearcave.com/misVmisl_tech/wavelets/packfreq/index.html
- O. Rioul and M. Vetterli, "Wavelets and signal processing," IEEE Signal Processing Mag. vol. 8, pp.14-38, Oct. 1991
- C.J. Li , & J. Ma,. "Wavelet decomposition of vibrations for detection of bearing-localized defects." NDT&E Inlernalional, vol. 30, pp. 143-149.1997
유료 다운로드의 경우 해당 사이트의 정책에 따라 신규 회원가입, 로그인, 유료 구매 등이 필요할 수 있습니다. 해당 사이트에서 발생하는 귀하의 모든 정보활동은 NDSL의 서비스 정책과 무관합니다.
원문복사신청을 하시면, 일부 해외 인쇄학술지의 경우 외국학술지지원센터(FRIC)에서
무료 원문복사 서비스를 제공합니다.
NDSL에서는 해당 원문을 복사서비스하고 있습니다. 위의 원문복사신청 또는 장바구니 담기를 통하여 원문복사서비스 이용이 가능합니다.
- 이 논문과 함께 출판된 논문 + 더보기