A study on the fault diagnosis of rotating machine by machine learning

Abstract

In this study, a rotating machine that can reproduce normal condition and 8 fault conditions were produced, and vibration data was acquired. Feature is calculated from the acquired data, and accuracy is analyzed through fault diagnosis using artificial neural networks and genetic algorithms. In order to achieve optimal timing and higher accuracy, features by three domains were applied to the fault diagnosis. The learning number was selected as a setting variable. As a result of the rotating machine fault diagnosis, high precision was found in the frequency domain than in others, and precise fault diagnoses were accomplished through all of 10 operations, at the learning number of 5000 and 8000. Given the efficiency of time, it was estimated to be the most efficient when the number of learning was 5000.