Statistical Approach to Diagnostic Rules for Various Malfunctions of Journal Bearing System Using Fisher Discriminant Analysis
This research is focused on developing an efficient fault diagnosis procedure for a journal bearing system. Vibration data of journal bearing rotor simulator under four conditions (i.e. a normal condition and three anomaly conditions including unbalance, rubbing and misalignment) was used to develop the algorithm. In order to improve diagnostic performance, cycle based time-domain features and frequency-domain features were extracted after resampling process being applied to the raw vibration data. Then, the optimal feature selection was accomplished by mixture of random combination performance test and Fisher Discrimin- ant Ratio (FDR) criteria. After selecting optimal features, Fisher Discriminant Analysis (FDA) algorithm classified each abnormal conditions mentioned above. To end with, the result of classification is evaluated and verified
How to Cite
Al-Raheem, K.F., & Abdul-Karem, W. (2010). Rolling bearing fault diagnostics using artificial neural networks based on laplace wavelet analysis. International Journal of Engineering, Science and Technology, 2(6).
Chen, C., & Mo, C. (2004). A method for intelligent fault diagnosis of rotating machinery. Digital Signal Processing, 14(3), 203-217.
Gupta, K. (1997). Vibration—a tool for machine diagnostics and condition monitoring. Sadhana, 22(3), 393-410.
Han, T., Yang, B.-S., Choi, W.-H., & Kim, J.-S. (2006). Fault diagnosis system of induction motors based on neural network and genetic algorithm using stator current signals. International Journal of Rotating Machinery, 2006.
Huo-Ching, S., & Yann-Chang, H. (2012, 4-6 June 2012). Fault diagnosis of steam turbine-generator sets using evolutionary based support vector machine. Paper presented at the Computer, Consumer and Control (IS3C), 2012 International Symposium on.
Jardine, A.K., Lin, D., & Banjevic, D. (2006). A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical systems and signal processing, 20(7), 1483-1510. doi: DOI 10.1016/j.ymssp.2005.09.012
Lei, Y., He, Z., & Zi, Y. (2008). A new approach to intelligent fault diagnosis of rotating machinery. Expert Systems with Applications, 35(4), 1593-1600.
Lei, Y., He, Z., Zi, Y., & Chen, X. (2008). New clustering algorithm-based fault diagnosis using compensation distance evaluation technique. Mechanical Systems and Signal Processing, 22(2), 419-435.
Lei, Y., He, Z., Zi, Y., & Hu, Q. (2007). Fault diagnosis of rotating machinery based on multiple anfis combination with gas. Mechanical Systems and Signal Processing, 21(5), 2280-2294. doi: DOI 10.1016/j.ymssp.2006.11.003
Li, B., Chow, M.-Y., Tipsuwan, Y., & Hung, J.C. (2000). Neural-network-based motor rolling bearing fault diagnosis. Industrial Electronics, IEEE Transactions on, 47(5), 1060-1069.
Ocak, H., Loparo, K.A., & Discenzo, F.M. (2007). Online tracking of bearing wear using wavelet packet decomposition and probabilistic modeling: A method for bearing prognostics. Journal of sound and vibration, 302(4), 951-961.
Randall, R.B., & Antoni, J. (2011). Rolling element bearing diagnostics—a tutorial. Mechanical Systems and Signal Processing, 25(2), 485-520.
Salahshoor, K., Kordestani, M., & Khoshro, M.S. (2010). Fault detection and diagnosis of an industrial steam turbine using fusion of svm (support vector machine) and anfis (adaptive neuro-fuzzy inference system) classifiers. Energy, 35(12), 5472-5482.
Samanta, B., & Al-Balushi, K. (2003). Artificial neural network based fault diagnostics of rolling element bearings using time-domain features. Mechanical systems and signal processing, 17(2), 317-328.
Samanta, B., Al-Balushi, K., & Al-Araimi, S. (2003). Artificial neural networks and support vector machines with genetic algorithm for bearing fault detection. Engineering Applications of Artificial Intelligence, 16(7), 657-665.
Sanz, J., Perera, R., & Huerta, C. (2007). Fault diagnosis of rotating machinery based on auto-associative neural networks and wavelet transforms. Journal of Sound and Vibration, 302(4), 981-999.
Theodoridis, S., & Koutroumbas, K. (2008). Pattern recognition: Elsevier Science.
Theodoridis, S., & Theodoridis, S. (2010). Introduction to pattern recognition : A matlab approach. Burlington, MA: Academic Press.
Wei, X., Guo, K., Jia, L., Liu, G., & Yuan, M. (2013). Fault isolation of light rail vehicle suspension system based on ds evidence theory and improvement application case. Journal of Intelligent Learning Systems & Applications, 5(4).
Welling, M. (2005). Fisher linear discriminant analysis. Department of Computer Science, University of Toronto.
Wong, M., Jack, L., & Nandi, A. (2006). Modified self-organising map for automated novelty detection applied to vibration signal monitoring. Mechanical Systems and Signal Processing, 20(3), 593-610.
Yang, B.-S., Han, T., & An, J.L. (2004). Art–kohonen neural network for fault diagnosis of rotating machinery. Mechanical Systems and Signal Processing, 18(3), 645-657.
Yang, B.-S., Han, T., & Hwang, W.-W. (2005). Fault diagnosis of rotating machinery based on multi-class support vector machines. Journal of Mechanical Science and Technology, 19(3), 846-859.
Yang, B.-S., & Widodo, A. (2009). Introduction of intelligent machine fault diagnosis and prognosis: Nova Science Publishers.
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.