Statistical Approach to Diagnostic Rules for Various Malfunctions of Journal Bearing System Using Fisher Discriminant Analysis

##plugins.themes.bootstrap3.article.main##

##plugins.themes.bootstrap3.article.sidebar##

Published Jul 8, 2014
Byungchul Jeon Joonha Jung Byeng D. Youn Yeonwhan Kim Yong-Chae Bae

Abstract

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

Jeon, B., Jung, J., Youn, B. D., Kim, Y., & Bae, Y.-C. (2014). Statistical Approach to Diagnostic Rules for Various Malfunctions of Journal Bearing System Using Fisher Discriminant Analysis. PHM Society European Conference, 2(1). https://doi.org/10.36001/phme.2014.v2i1.1546
Abstract 578 | PDF Downloads 145

##plugins.themes.bootstrap3.article.details##

Keywords

PHM

References
Ahmadi, H., Moosavian, A., & Khazaee, M. (2012). An appropriate approach for misalignment fault diagnosis based on feature selection and least square support vector machine.
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.
Section
Technical Papers

Similar Articles

<< < 1 2 3 4 5 6 7 8 9 10 > >> 

You may also start an advanced similarity search for this article.