Fault diagnosis based on the quantification of the fault features in a rotary machine

Abstract Fault diagnosis of rotary machinery is essential to fixing defective rotary machines and preventing rotary systems from breaking. Recently, fault diagnosis techniques have moved from traditional methods to artificial intelligence techniques. Many research groups have reported on the development of various artificial intelligence-based classifiers to improve diagnostic performance. In classifier design, selecting the datasets that include standard or fault features is essential to obtaining a high-performance classifier. However, there are few studies on the techniques to evaluate the quality of datasets numerically. In this study, we have developed a fault severity criterion to quantify the faultless and fault features of measured data. Using the proposed criterion, we have determined the dominant direction of representative faults in a rotary machine. The standard and fault data have been obtained, considering the dominant direction of each fault. The intrinsic mode function (IMF) that presents the fault features has been obtained by an empirical mode decomposition and a sensitive IMF selection criterion. Finally, we have designed an accurate and memory-efficient classifier using the extracted data and verified its performance by diagnosing a 7.5 kW servo motor. A conventional support vector machine has been used to verify the effects of the proposed algorithm on the classifier’s performance improvement. The developed classifier demonstrated an increase of performance up to an average of 51.9%, compared with the classifiers using training datasets measured in an arbitrary direction, with the detection rate of 99.9%. The study results suggest that the proposed classifier design technique based on the quantification of the fault features is useful for creating high-quality training datasets, machine learning, and deep learning-based classifiers.