Fault-Diagnosis for Reciprocating Compressors Using Big Data
Fault-Diagnosis for Reciprocating Compressors Using Big Data
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DOI：
10.1109/bigdataservice.2016.27
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发表时间：
2016-03
期刊：
影响因子：
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通讯作者：
Ke Erqinhu;Guanqiu Qi;W. Tsai;Yi Hong;Wenxian Wang;Guangxin Hou;Zhiqin Zhu
中科院分区：
文献类型：
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作者：
Ke Erqinhu;Guanqiu Qi;W. Tsai;Yi Hong;Wenxian Wang;Guangxin Hou;Zhiqin Zhu
往复式压缩机广泛应用于石油工业，往复式压缩机中的一个小故障可能会导致运行中的严重问题。监测和检测潜在故障有助于压缩机继续正常运行。本文提出了一个压缩机故障诊断系统，利用机器学习技术来检测潜在的故障。该系统采用中国海洋石油总公司100 TB的运行数据进行了评估，首先对数据进行去噪、编码，然后应用SVM分类，其中50%的数据用于训练，其余用于测试。结果表明，该系统可以有效地诊断压缩机的潜在故障，80%的准确率。
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.