Fault-diagnosis for reciprocating compressors using big data and machine learning

Abstract Reciprocating compressors are widely used in petroleum industry. A small fault in reciprocating compressor may cause serious issues in operation. Traditional regular maintenance and fault diagnosis solutions cannot efficiently detect potential faults in reciprocating compressors. 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 compressors with more than 80% accuracy, which represents a better result than the current practice.

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