- About this Journal ·
- Abstracting and Indexing ·
- Advance Access ·
- Aims and Scope ·
- Annual Issues ·
- Article Processing Charges ·
- Articles in Press ·
- Author Guidelines ·
- Bibliographic Information ·
- Citations to this Journal ·
- Contact Information ·
- Editorial Board ·
- Editorial Workflow ·
- Free eTOC Alerts ·
- Publication Ethics ·
- Reviewers Acknowledgment ·
- Submit a Manuscript ·
- Table of Contents
Advances in Mechanical Engineering
Volume 2012 (2012), Article ID 518468, 8 pages
Fault Severity Estimation of Rotating Machinery Based on Residual Signals
School of Mechanical Engineering, China University of Mining and Technology, Xuzhou 221116, China
Received 29 July 2012; Accepted 17 September 2012
Academic Editor: C. S. Shin
Copyright © 2012 Fan Jiang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
- G. F. Bin, J. J. Gao, X. J. Li, and B. S. Dhillon, “Early fault diagnosis of rotating machinery based on waveletpackets-empirical mode decomposition feature extraction and neural network,” Mechanical Systems and SignalProcessing, vol. 27, pp. 696–711, 2012.
- I. Aydin, M. Karakose, and E. Akin, “A new method for early fault detection and diagnosis of broken rotor bars,” Energy Conversion and Management, vol. 52, no. 4, pp. 1790–1799, 2011.
- J. Yu, “Bearing performance degradation assessment using locality preserving projections and Gaussian mixture models,” Mechanical Systems and Signal Processing, vol. 25, no. 7, pp. 2573–2588, 2011.
- A. M. Al-Ghamd and D. Mba, “A comparative experimental study on the use of acoustic emission and vibration analysis for bearing defect identification and estimation of defect size,” Mechanical Systems and Signal Processing, vol. 20, no. 7, pp. 1537–1571, 2006.
- R. Huang, L. Xi, X. Li, C. Richard Liu, H. Qiu, and J. Lee, “Residual life predictions for ball bearings based on self-organizing map and back propagation neural network methods,” Mechanical Systems and Signal Processing, vol. 21, no. 1, pp. 193–207, 2007.
- P. W. Tse and D. P. Atherton, “Prediction of machine deterioration using vibration based fault trends and recurrent neural networks,” Journal of Vibration and Acoustics, vol. 121, no. 3, pp. 355–362, 1999.
- Z. Tian, L. Wong, and N. Safaei, “A neural network approach for remaining useful life prediction utilizing both failure and suspension histories,” Mechanical Systems and Signal Processing, vol. 24, no. 5, pp. 1542–1555, 2010.
- H. Hong and M. Liang, “Fault severity assessment for rolling element bearings using the Lempel-Ziv complexity and continuous wavelet transform,” Journal of Sound and Vibration, vol. 320, no. 1-2, pp. 452–468, 2009.
- I. Yesilyurt, F. Gu, and A. D. Ball, “Gear tooth stiffness reduction measurement using modal analysis and its use in wear fault severity assessment of spur gears,” NDT and E International, vol. 36, no. 5, pp. 357–372, 2003.
- Z. X. Li, X. P. Yan, C. Q. Yuan, J. B. Zhao, and Z. X. Peng, “Fault detection and diagnosis of a gearbox in marine propulsion systems using bispectrum analysis and artificial neural networks,” Journal of Marine Science and Application, vol. 10, no. 1, pp. 17–24, 2011.
- C. C. Lin and H. P. Wang, “Performance analysis of rotating machinery using enhanced cerebellar model articulation controller (E-CMAC) neural networks,” Computers and Industrial Engineering, vol. 30, no. 2, pp. 227–242, 1996.
- C. T. Yiakopoulos, K. C. Gryllias, and I. A. Antoniadis, “Rolling element bearing fault detection in industrial environments based on a K-means clustering approach,” Expert Systems with Applications, vol. 38, no. 3, pp. 2888–2911, 2011.
- L. Zhang, G. L. Xiong, H. S. Liu, H. J. Zou, and W. Z. Guo, “Bearing fault diagnosis using multi-scale entropy and adaptive neuro-fuzzy inference,” Expert Systems with Applications, vol. 37, no. 8, pp. 6077–6085, 2010.
- N. Sawalhi, R. B. Randall, and H. Endo, “The enhancement of fault detection and diagnosis in rolling element bearings using minimum entropy deconvolution combined with spectral kurtosis,” Mechanical Systems and Signal Processing, vol. 21, no. 6, pp. 2616–2633, 2007.
- R. Ma, Y. S. Chen, and Q. J. Cao, “Research on dynamics and fault mechanism of spur gear pair with spallingdefect,” Journal of Sound and Vibration, vol. 331, no. 9, pp. 2097–2109, 2012.
- E. J. Manders and G. Biswas, “FDI of abrupt faults with combined statistical detection and estimation andqualitative fault isolation,” in Proceedings of the 5th Symposium on Fault Detection, Supervision and Safety for Technical Processes, pp. 347–352, Washington, DC, USA, 2003.
- J. Rafiee, M. A. Rafiee, and P. W. Tse, “Application of mother wavelet functions for automatic gear and bearing fault diagnosis,” Expert Systems with Applications, vol. 37, no. 6, pp. 4568–4579, 2010.
- P. F. J. Burgess, “Antifriction bearing fault detection using envelope detection,” Transactions of the Institution of Professional Engineers New Zealand: Electrical, Mechanical, and Chemical Engineering Section, vol. 15, no. 2, pp. 77–82, 1988.
- N. Baydar and A. Ball, “Detection of gear deterioration under varying load conditions by using the instantaneous power spectrum,” Mechanical Systems and Signal Processing, vol. 14, no. 6, pp. 907–921, 2000.
- M. S. Lambert, T. T. Mariam, and F. H. Susan, Parseval’s Theorem, VDM, Dr. Mueller AG & Co. Kg, 2010.
- Z. K. Peng, F. L. Chu, and P. W. Tse, “Singularity analysis of the vibration signals by means of wavelet modulus maximal method,” Mechanical Systems and Signal Processing, vol. 21, no. 2, pp. 780–794, 2007.
- L. B. Zhang, Z. H. Wang, and S. X. Zhao, “Short-term fault prediction of mechanical rotating parts on the basis of fuzzy-grey optimising method,” Mechanical Systems and Signal Processing, vol. 21, no. 2, pp. 856–865, 2007.
- Case Western Reserve University, Bearing Data Center (Seeded Fault Test Data), http://csegroups.case.edu/bearingdatacenter/home.