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http://dx.doi.org/10.5762/KAIS.2018.19.1.75

A Study of Big data-based Machine Learning Techniques for Wheel and Bearing Fault Diagnosis  

Jung, Hoon (Hyper-connected Communication Research Lab., Postal Technology Research Center, ETRI)
Park, Moonsung (Hyper-connected Communication Research Lab., Postal Technology Research Center, ETRI)
Publication Information
Journal of the Korea Academia-Industrial cooperation Society / v.19, no.1, 2018 , pp. 75-84 More about this Journal
Abstract
Increasing the operation rate of components and stabilizing the operation through timely management of the core parts are crucial for improving the efficiency of the railroad maintenance industry. The demand for diagnosis technology to assess the condition of rolling stock components, which employs history management and automated big data analysis, has increased to satisfy both aspects of increasing reliability and reducing the maintenance cost of the core components to cope with the trend of rapid maintenance. This study developed a big data platform-based system to manage the rolling stock component condition to acquire, process, and analyze the big data generated at onboard and wayside devices of railroad cars in real time. The system can monitor the conditions of the railroad car component and system resources in real time. The study also proposed a machine learning technique that enabled the distributed and parallel processing of the acquired big data and automatic component fault diagnosis. The test, which used the virtual instance generation system of the Amazon Web Service, proved that the algorithm applying the distributed and parallel technology decreased the runtime and confirmed the fault diagnosis model utilizing the random forest machine learning for predicting the condition of the bearing and wheel parts with 83% accuracy.
Keywords
Big data; Bearing/Wheel; Fault Diagnosis; Machine Learning; Rolling Stock;
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Times Cited By KSCI : 2  (Citation Analysis)
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1 S. Ha, S. Chang, and W. Yoo, "Development of the Preventive Maintenance System for an Urban Transit," Journal of the Society of Korea Indus. and Sys. Eng, vol. 30, no. 1, pp. 1-7, 2007.
2 Y. Cho, J. Kim, and J. Kim, "Performance Improvement of Bearing Fault Diagnosis Using a Real-time Training Method," Journal of Multimedia Services Convergent, vol. 7, no. 4, pp. 551-559, 2017. DOI: http://dx.doi.org/10.14257/AJMAHS.2017.04.33   DOI
3 F. Li, B. C. Ooi, M. T. Ozsu, and S. W. Zhejiang, "Distributed data management using MapReduce," ACM Computing Surveys, vol. 6, no. 3, 2014. DOI: https://doi.org/10.1145/2503009   DOI
4 H. G. Lee, Y. H. Choi, J. Hoon, and Y. H. Shin, "Spatial Subspace Projected Clustering Method & MapReduce based Temporal Mining for Direct Marketing Service in Korea Post," ETRI Journal, vol. 37, no. 2, 2015. DOI: https://doi.org/10.4218/etrij.15.2314.0068   DOI
5 Y. HE, H. TAN, W. LUO, S. FENG, and J. FAN, "MR-DBSCAN: a scalable MapReduce -based DBSCAN algorithm for heavily skewed data," Front. Comput. Sci., vol. 8, no. 1, pp. 83-99, 2014. DOI: https://doi.org/10.1007/s11704-013-3158-3   DOI
6 H. Jung, and J. Kim, "A Machine Learning Approach for Mechanical Motor Fault Diagnosis," Society of Korea Indus. and Sys. Eng., vol. 40, no. 1, pp. 57-64, 2017. DOI: https://doi.org/10.11627/jkise.2017.40.1.057   DOI
7 S. Tsuchiya, R. Yumiba, Y. Yamauchi, T. Yamashita, and H. Fujiyoshi, "Transfer forest based on covariate shift," Proc. of IAPR Asian Conf. on Pattern Recognition, 2015. DOI: https://doi.org/10.1109/ACPR.2015.7486605   DOI
8 S. Rio, V. Lopez, J. M. Benitez, and F. Herrera, "On the use of MapReduce for imbalanced big data using Random Forest," Journal of Information Sciences, vol. 285, pp. 112-137, 2014. DOI: https://doi.org/10.1016/j.ins.2014.03.043   DOI