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Air Compressor Fault Diagnosis Through Vibration Signals using Statistical Features and J48 Algorithms
Objectives: The fault diagnosis in reciprocating air compressor system was done through this article using vibration signals from accelerometer for both healthy and faulty conditions. Methods/Analysis: This article presents a condition monitoring strategy for compressor through vibration signals using accelerometer data in identifying five common faults of air compressor these were simulated manually. These vibration signals were processed through machine learning technique, where statistical features were extracted and the features contributing to the maximum classification accuracy were selected. The J48 decision tree algorithm is used in predicting the compressor faults in early stages. Findings: High classification accuracy of 98.33% was obtained for fault detection in compressor system. Application/Improvements: The proposed model can be used for regular monitoring of air compressor.
Fault Diagnosis, J48 Algorithms, Reciprocating Air Compressor, Vibration Signals.
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