Diagnosis of air compressor condition using minimum redunduncy maximum relevance (MRMR) algorithim and distance metric based classification

Finding a reliable machines condition monitoring technique has been attracted many researchers to avoid the sudden failure in machines and the unexpected consequences. This work proposes a fault diagnosis of air compressors using frequency-based features and distance metric-based classification. The analyzed experimental datasets contain one healthy condition and seven different fault conditions. Features are extracted from the frequency spectrum, then the best feature sets are selected using MRMR algorithm and eventually the classification is conducted using a distance metric classifier. The results demonstrated the automatic classification with more than 97% correct classification rate. The effect of selected feature set size, training sample size on the classification accuracy is also investigated. From the results, this method of analysis can be used for early detection of faults with very great accuracy.