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دسته بندی:
داده های بزرگ - big data
سال انتشار:
2018
عنوان انگلیسی مقاله:
Fault-diagnosis for reciprocating compressors using big data and machine learning
ترجمه فارسی عنوان مقاله:
تشخیص گسل برای کمپرسورهای مجاور با استفاده از داده های بزرگ و یادگیری ماشین
منبع:
Sciencedirect - Elsevier - Simulation Modelling Practice and Theory, 80 (2018) 104-127: doi:10:1016/j:simpat:2017:10:005
نویسنده:
Guanqiu Qi a,b, Zhiqin Zhu a,∗, Ke Erqinhu c, Yinong Chen b, Yi Chai d, Jian Sun e,d
چکیده انگلیسی:
Reciprocating compressors are widely used in petroleum industry. A small fault in recipro
cating compressor may cause serious issues in operation. Traditional regular maintenance
and fault diagnosis solutions cannot efficiently detect potential faults in reciprocating com
pressors. This paper proposes a fault-diagnosis system for reciprocating compressors. It
applies machine-learning techniques to data analysis and fault diagnosis. The raw data is
denoised first. Then the denoised data is sparse coded to train a dictionary. Based on the
learned dictionary, potential faults are finally recognized and classified by support vector
machine (SVM). The system is evaluated by using 5-year operation data collected from an
offshore oil corporation in a cloud environment. The collected data is evenly divided into
two halves. One half is used for training, and the other half is used for testing. The results
demonstrate that the proposed system can efficiently diagnose potential faults in com
pressors with more than 80% accuracy, which represents a better result than the current
practice.
Keywords: Reciprocating compressor، Big data ، Cloud computing ، Deep learning ، RPCA ، SVM
قیمت: رایگان
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