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Fault diagnosis of rotating machine by thermography method on support vector machine
임강민,배동명,김주형 대한기계학회 2014 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.28 No.8
Feature-based classification techniques consist of data acquisition, preprocessing, feature representation, feature calculation, feature selection,and classifiers. They are useful for online, real-time condition monitoring and fault diagnosis / features, which are now availablewith the development of information technologies and various measurement techniques. In this paper, an intelligent feature-based faultdiagnosis is suggested, developed, and compared with vibration signals and thermal images. Fault diagnosis is performed using thermalimaging along with support vector machine (SVM) classification to simulate machinery faults, resulting in an accuracy level comparableto vibration signals. The observed results show that fault diagnosis using thermal images for rotating machines can be applied to industrialareas as a novel intelligent fault diagnostic method with plausible accuracy. It can be also proposed as a unique non-contact methodto analyze rotating systems in mass production lines within a short time.