@inproceedings{453309cb14664c79a23882b363c1b7a0,
title = "Fault-Diagnosis for Reciprocating Compressors Using Big Data",
abstract = "Reciprocating compressors are widely used in the petroleum industry, and a small fault in reciprocating compressors may cause serious issues in operation. Monitoring and detecting potential faults help compressors to continue normal operation. This paper proposes a fault-diagnosis system for compressors using machine-learning techniques to detect potential faults. The system has been evaluated using 100TB operation data collected from China National Offshore Oil Corporation, and the data are first de-noised, coded, and then SVM classification is applied, with 50\% of data used for training, the remaining for testing. The results demonstrated that the system can efficiently diagnose potential faults in compressors with 80\% accuracy.",
keywords = "Big Data, RPCA, Reciprocating Compressor, SVM",
author = "Keerqinhu and Guanqiu Qi and Tsai, \{Wei Tek\} and Yi Hong and Wenxiang Wang and Guangxin Hou and Zhiqin Zhu",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2nd IEEE International Conference on Big Data Computing Service and Applications, BigDataService 2016 ; Conference date: 29-03-2016 Through 01-04-2016",
year = "2016",
month = may,
day = "19",
doi = "10.1109/BigDataService.2016.27",
language = "英语",
series = "Proceedings - 2016 IEEE 2nd International Conference on Big Data Computing Service and Applications, BigDataService 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "72--81",
booktitle = "Proceedings - 2016 IEEE 2nd International Conference on Big Data Computing Service and Applications, BigDataService 2016",
address = "美国",
}