Exploring Pattern Recognition for Bearing Fault Diagnosis
DOI:
https://doi.org/10.29313/statistika.v22i2.1128Keywords:
bearing diagnosis, pattern recognition, principal component, Hotelling T-squareAbstract
Traditional bearing sensory diagnostic include touching and hearing rely on personal experience, and for more complex system are unable to meet the needs of equipment fault diagnosis. The research on bearing fault diagnosis is developing significantly. Bearings are used in rotating machinery and most machinery failures are caused by bearing failures. The fault diagnosis of bearings is an important research area. The core of bearing fault diagnosis is the pattern recognition of fault features. The key of pattern recognition is to develop a reasonable classifier. Intelligent pattern recognition has been developed such as principal components, support vector machine, neural network. In this study, a bearing fault diagnosis based on exploring pattern recognition is proposed. The key to pattern recognition is to design a significant classifier. A number of features from bearing vibration of normal and fault bearing are extracted and processed using principal components of correlation matrix. Plot of principal components shows the visualization of normal and fault bearing and the classifier is chosen subjectively. The principal components exploration will be confirmed using least squares support vector machine. The parameter of support vector machine estimated using heuristic optimization particle swarm optimization. The proposed method can be applied in the detection of faults of bearing
References
Bugharbee, Hussein Al, and Irina Trendafilova. 2018. “A New Methodology for Fault Detection in Rolling Element Bearings Using Singular Spectrum Analysis.” MATEC Web of Conferences 148: 0–4. https://doi.org/10.1051/matecconf/201814814002.
Elsamanty, M., W. S. Salman, and A. A. Ibrahim. 2021. “Rotary Machines Fault Diagnosis Based on Principal Component Analysis.” Engineering Research Journal 171 (0): 138–50. https://doi.org/10.21608/erj.2021.193822.
Holmes, Susan. 2008. “Multivariate Data Analysis: The French Way.” Probability and Statistics: Essays in Honor of David A. Freedman 2: 219–33. https://doi.org/10.1214/193940307000000455.
Jin, Xiaohang, Jicong Fan, and Tommy W.S. Chow. 2019. “Fault Detection for Rolling-Element Bearings Using Multivariate Statistical Process Control Methods.” IEEE Transactions on Instrumentation and Measurement 68 (9): 3128–36136. https://doi.org/10.1109/TIM.2018.2872610.
Kirankumar, M. V., M. Lokesha, Sujesh Kumar, and Ajith Kumar. 2018. “Review on Condition Monitoring of Bearings Using Vibration Analysis Techniques.” IOP Conference Series: Materials Science and Engineering 376 (1). https://doi.org/10.1088/1757-899X/376/1/012110.
Li, Hui, and Yang Qi. 2020. “EMD and Singular Value Difference Spectrum Based Bearing Fault Characteristics Extraction.” Journal of Physics: Conference Series 1624 (3). https://doi.org/10.1088/1742-6596/1624/3/032004.
Liu, Zhiliang, Huan Wang, Junjie Liu, Yong Qin, and Dandan Peng. 2021. “Multitask Learning Based on Lightweight 1DCNN for Fault Diagnosis of Wheelset Bearings.” IEEE Transactions on Instrumentation and Measurement 70 (August). https://doi.org/10.1109/TIM.2020.3017900.
Mardia, K V, J T Kent, and J M Bibby. 1979. Multivariate Analisys. Multivariate Analisys.
Pang, Bin, Guiji Tang, and Tian Tian. 2019. “Enhanced Singular Spectrum Decomposition and Its Application to Rolling Bearing Fault Diagnosis.” IEEE Access 7: 87769–82. https://doi.org/10.1109/ACCESS.2019.2924962.
Prakash Yadav, Om, and G L Pahuja. 2019. “Bearing Fault Detection Using Logarithmic Wavelet Packet Transform and Support Vector Machine.” International Journal of Image, Graphics and Signal Processing 11 (5): 21–33. https://doi.org/10.5815/ijigsp.2019.05.03.
Sarwar, Muhammad, Faisal Mehmood, Muhammad Abid, Abdul Qayyum Khan, Sufi Tabassum Gul, and Adil Sarwar Khan. 2020. “High Impedance Fault Detection and Isolation in Power Distribution Networks Using Support Vector Machines.” Journal of King Saud University - Engineering Sciences 32 (8): 524–35. https://doi.org/10.1016/j.jksues.2019.07.001.
Tharrault, Yvon, Gilles Mourot, José Ragot, and Didier Maquin. 2008. “Fault Detection and Isolation with Robust Principal Component Analysis.” International Journal of Applied Mathematics and Computer Science 18 (4): 429–42. https://doi.org/10.2478/v10006-008-0038-3.
Yuan, Yu, and Chen Chen. 2020. “Fault Detection of Rolling Bearing Based on Principal Component Analysis and Empirical Mode Decomposition.” AIMS Mathematics 5 (6): 5916–38. https://doi.org/10.3934/math.2020379.
Zhao, Huimin, Jianjie Zheng, Junjie Xu, and Wu Deng. 2019. “Fault Diagnosis Method Based on Principal Component Analysis and Broad Learning System.” IEEE Access 7: 99263–72. https://doi.org/10.1109/ACCESS.2019.2929094.
Zhou, Fenfang, Li Xia, Wei Dong, Xinya Sun, Xiang Yan, and Qianchuan Zhao. 2016. “Fault Diagnosis of High-Speed Railway Turnout Based on Support Vector Machine.” Proceedings of the IEEE International Conference on Industrial Technology 2016-May (December 2018): 1539–44. https://doi.org/10.1109/ICIT.2016.7474989.
