- 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 ·
- Subscription Information ·
- Table of Contents
International Journal of Distributed Sensor Networks
Volume 2013 (2013), Article ID 472675, 10 pages
Concurrent Fault Diagnosis for Rotating Machinery Based on Vibration Sensors
1Guangdong Petrochemical Equipment Fault Diagnosis Key Laboratory, Guangdong University of Petrochemical Technology, Maoming 525000, China
2School of Automation, Guangdong University of Technology, Guangzhou 510006, China
3Department of Automation, Tsinghua University, Beijing 100084, China
Received 10 January 2013; Accepted 5 April 2013
Academic Editor: Zhangbing Zhou
Copyright © 2013 Qing-Hua Zhang 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.
- Y. G. Lei, Z. J. He, and Y. Y. Zi, “A new approach to intelligent fault diagnosis of rotating machinery,” Expert Systems with Applications, vol. 35, no. 4, pp. 1593–1600, 2008.
- J. Lin and L. S. Qu, “Feature extraction based on morlet wavelet and its application for mechanical fault diagnosis,” Journal of Sound and Vibration, vol. 234, no. 1, pp. 135–148, 2000.
- X. S. Si, C. H. Hu, J. B. Yang, and Q. Zhang, “On the dynamic evidential reasoning algorithm for fault prediction,” Expert Systems with Applications, vol. 38, no. 5, pp. 5061–5080, 2011.
- X. S. Si, C. H. Hu, J. B. Yang, and Z. J. Zhou, “A new prediction model based on belief rule base for system behavior prediction,” IEEE Transactions on Fuzzy Systems, vol. 19, no. 4, pp. 456–471, 2011.
- G. Z. Dai, Q. Pan, S. Y. Zhang, and H. C. Zhang, “The developments and problems in evidence reasoning,” Control Theory and Applications, vol. 16, no. 4, pp. 465–469, 1999.
- P. K. Harmer, P. D. Williams, G. H. Gunsch, and G. B. Lamont, “An artificial immune system architecture for computer security applications,” IEEE Transactions on Evolutionary Computation, vol. 6, no. 3, pp. 252–280, 2002.
- X. F. Fan and M. J. Zuo, “Fault diagnosis of machines based on D-S evidence theory. Part 2: application of the improved D-S evidence theory in gearbox fault diagnosis,” Pattern Recognition Letters, vol. 27, no. 5, pp. 377–385, 2006.
- Z. H. Han, F. Wang, X. D. Hao, and S. Liu, “Fault diagnosis of turbine vibration based on artificial immune algorithm,” Journal of North China Electric Power University, vol. 37, no. 3, pp. 38–42, 2010.
- Y. Peng, C. L. Zhang, H. Zhao, and X. Yue, “Fault diagnosis of nuclear equipment based on artificial immune system,” Nuclear Power Engineering, vol. 29, no. 2, pp. 124–128, 2008.
- P. Zhao and Z. S. Wang, “Aero-engine rotor fault diagnodis based on dempster-shafer evidential theory,” Machinery Design & Manufacture, vol. 1, pp. 136–137, 2008.
- Q. H. Meng, X. J. Zhou, and Y. C. Wu, “Vehicle fault diagnosis based on wavelet-immune system,” Automotive Engineering, vol. 26, no. 5, pp. 619–622, 2004.
- M. A. K. Jaradat and R. Langari, “A hybrid intelligent system for fault detection and sensor fusion,” Applied Soft Computing, vol. 9, no. 1, pp. 415–422, 2009.
- I. Aydin, M. Karakose, and E. Akin, “A multi-objective artificial immune algorithm for parameter optimization in support vector machine,” Applied Soft Computing, vol. 11, no. 1, pp. 120–129, 2011.
- C. J. Wang, S. X. Xia, and Q. Niu, “Artificial immune particle swarm optimization for fault diagnosis of mine hoist,” Acta Electronica Sinica, vol. 38, no. 2, pp. 94–98, 2010.
- W. L. Jiang, H. F. Niu, and S. Y. Liu, “Composite fault diagnosis method and its verification experiments,” Journal of Vibration and Shock, vol. 30, no. 6, pp. 176–180, 2011.
- Z. Wanjun, W. Xin, and L. Xinliang, “Mixed diagnosis tactic on fuzzy-immunity of gun-launched missile system,” Ordnance Industry Automation, vol. 31, no. 16, pp. 1–64, 2012.
- T. Yüksel and A. Sezgin, “Two fault detection and isolation schemes for robot manipulators using soft computing techniques,” Applied Soft Computing, vol. 10, no. 1, pp. 125–134, 2010.
- S. Sampath and R. Singh, “An integrated fault diagnostics model using genetic algorithm and neural networks,” Journal of Engineering for Gas Turbines and Power, vol. 128, no. 1, pp. 49–56, 2006.
- R. Naresh, V. Sharma, and M. Vashisth, “An integrated neural fuzzy approach for fault diagnosis of transformers,” IEEE Transactions on Power Delivery, vol. 23, no. 4, pp. 2017–2024, 2008.
- Y. Y. Wang and Q. J. Li, “Research on fault diagnosis expert system based on the neural network and the fault tree technology,” Procedia Engineering, vol. 31, pp. 1206–1210, 2012.
- S. W. Fei and X. B. Zhang, “Fault diagnosis of power transformer based on support vector machine with genetic algorithm,” Expert Systems with Applications, vol. 36, no. 8, pp. 11352–11357, 2009.
- L. Ping, “Fault diagnosis for motor rotor based on KPCA-SVM,” Applied Mechanics and Materials, vol. 143-144, pp. 680–684, 2011.
- H. Guo, L. B. Jack, and A. K. Nandi, “Feature generation using genetic programming with application to fault classification,” IEEE Transactions on Systems, Man, and Cybernetics B, vol. 35, no. 1, pp. 89–99, 2005.
- C. Pan, W. Chen, and Y. Yun, “Fault diagnostic method of power transformers based on hybrid genetic algorithm evolving wavelet neural network,” IET Electric Power Applications, vol. 2, no. 1, pp. 71–76, 2008.
- V. T. Tran, B.-S. Yang, M.-S. Oh, and A. C. C. Tan, “Fault diagnosis of induction motor based on decision trees and adaptive neuro-fuzzy inference,” Expert Systems with Applications, vol. 36, no. 2, part 1, pp. 1840–1849, 2009.
- B. Samanta and C. Nataraj, “Use of particle swarm optimization for machinery fault detection,” Engineering Applications of Artificial Intelligence, vol. 22, no. 2, pp. 308–316, 2009.
- C. F. Dong, X. B. Wei, and T. Y. Wang, “A new method for diagnosis research of compound faults rotor in turbine generator set,” Turbine Technology, vol. 45, no. 6, pp. 377–379, 2003.
- S. Forrest and S. A. Hofmeyr, “Immunology as information processing,” in Design Principles for the Immune System and Other Distributed Autonomous Systems, L. A. Segel and I. R. Cohen, Eds., Oxford University Press, New York, NY, USA, 2000.
- Q. H. Zhang, A method of rotating machinery fault diagnosis based on non-dimension immune detectors. China. Utility Model Patent. CN101000276 2007-07-18.
- Q. H. Zhang, The research on unit fault diagnosis technology based on artificial immune system [Ph.D. dissertation], South China University of Technology, Guangzhou, China, 2004.
- A. P. Dempster, “Upper and lower probabilities induced by a multi-valued mapping,” Annals Mathematical Statistics, vol. 38, pp. 325–339, 1967.
- G. Shafer, A Mathematical Theory of Evidence, Princeton University Press, 1976.
- J. P. Xuan, T. L. Shi, G. L. Liao, and W. X. Lai, “Classification feature extraction of multiple gear faults using genetic programming,” Journal of Vibration Engineering, vol. 19, no. 1, pp. 70–74, 2006.