Abstract:Rotating machines are widely used in modern industry. In a mechanical system, rolling bearings are essential. Bearings must be able to operate in extreme environments, in which they are prone to various faults. To address the challenge related to accurately classify bearing fault types using vibration sensors, we propose a convolutional Takagi-Sugeno-Kang (TSK)-type fuzzy neural network classifier (CTFNNC) that comprises a convolutional layer and a TSK-type fuzzy neural network. In the CTFNNC, convolutional la… Show more
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