Zhuo Wang
ABSTRACT
This paper aims to apply techniques of Big Data Analytics including K-Means Clustering to diagnose potential problems for offshore rotating machinery. The innovative methods are attempted in both Batch K-Means and Streaming K-Means. Their performances are compared with the conventional signal analysis method. Both K-Means models have a better performance on detecting significant mechanical faults as anomalies for offshore rotating machinery which can be considered as appropriate method for machine operational maintenance.
- Shouwei, Z., Xiaojian, J., Hengyi, Z., Yingnian, Z., Jiang, Z., Xuegang, Y., Keqin, C., Gang, B., Hua, G., and Chao, Z. "The facility and equipment of china offshore oil & gas industry," Engineering Sciences 5 (2010), 016.Google Scholar
- Guo, L. "Study on Risk Based Intelligent Maintenance Decision Making for Petrochemical Rotating Machinery," PhD thesis, Beijing University of Chemical Technology, 2009.Google Scholar
- Ji, X. "The application of big data in the key mobile equipment field of offshore oil platform," Science and Technology & Innovation, 16 (2017), 149--151.Google Scholar
- Ranjan, R. "Streaming big data processing in datacentre clouds," IEEE Cloud Computing 1, 1 (2014), 78--83.Google ScholarCross Ref
- Elmasri, R. "Fundamentals of database systems," Pearson Education India, 2008.Google Scholar
- Elahi, M., Li, K., Nisar, W., Lv, X., and Wang, H. "Efficient clustering-based outlier detection algorithm for dynamic data stream," In Fuzzy Systems and Knowledge Discovery, 2008. FSKD'08. Fifth International Conference on, vol. 5, IEEE (2008), 298--304.Google Scholar
- Kiss, I., Genge, B., Haller, P., and Sebestyen, G. "Data clustering-based anomaly detection in industrial control systems," In Intelligent Computer Communication and Processing (ICCP), 2014 IEEE International Conference on, IEEE (2014), 275--281.Google Scholar
- Kumari, R., Singh, M., Jha, R., Singh, N., et al. "Anomaly detection in network traffic using k-mean clustering," In Recent Advances in Information Technology (RAIT), 2016 3rd International Conference on, IEEE (2016), 387--393.Google Scholar
- Munz, G., Li, S., and Carle, G. "Traffic anomaly detection using k-means clustering," In GI/ITG Workshop MMBnet (2007).Google Scholar
- Kumari, R., Singh, M., Jha, R., Singh, N., et al. "Anomaly detection in network traffic using k-mean clustering," In Recent Advances in Information Technology (RAIT), 2016 3rd International Conference on, IEEE (2016), 387--393.Google Scholar
- Koupaie, H. M., Ibrahim, S., and Hosseinkhani, J. "Outlier detection in stream data by clustering method," International Journal of Advanced Computer Science and Information Technology 2, 3 (2013), 25--34.Google Scholar
Index Terms
- Anomaly detection for machinery by using Big Data Real-Time processing and clustering technique
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