Intelligent fault diagnosis of rotating machine elements using machine learning through optimal features extraction and selection

The rolling element bearings, and gears are the main components of rotating machines and are most prone to defects which may result in significant economic loss. The main purpose of this study is an automated diagnosis of rolling element bearings and gears defects using machine learning (ML) technique and statistical features extracted from time domain vibration signal and spectral kurtosis. Extracted features are used to train K- nearest neighbors (KNN) as diagnostic classifier. The significance of segmentation size for time domain raw vibrational signals for the purpose of feature extraction is studied. This analysis is carried out by varying the window/segment length for features extraction and observing its effect on classification accuracy. Importance of feature selection for optimal performance of KNN in defect classification is studied by selecting most important and useful features using Genetic Algorithm (GA). Furthermore, effect of value of K on performance of KNN classifier has been observed by varying the value of K between 1 to 10 with step size of 1. Results show the ability of KNN classifier in combination with GA for correct and confident fault diagnosis of rotating machine elements in case of proper selection of parameters for features extraction.