Optimal Feature Set for Detection of Inner Race Defect in Rolling Element Bearings
Rolling element bearings are the key components in many rotating machinery. It is necessary to determine the condition of the bearing with reasonable degree of confidence. Many techniques have been developed for bearing fault detection. Each of these techniques have their own strengths and weaknesses. In this paper various features are compared for detecting inner race defects in rolling element bearings. Mutual information between the feature and defect is used as a quantitative measure of quality and the features are ranked appropriately. Often, a combination of different features is used for bearing fault detection. Hence it is important to understand the interaction of features for classification purposes. This paper addresses this issue and determines the optimal feature set for best detection performance.
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bearings, damage detection, damage modeling
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