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• W4385816158 abstract "A rotating machine consists of many components, and the bearing is one of the key components in it. The health condition of the bearing directly affects the safe operation of the rotating machine and also inhibits the catastrophic loss to other components. To receive high-quality goods and services from these machines, their proper functioning requires intelligent and effective bearing diagnosis. Sound signal heard by a human is an important indicator of the faults in various components of the bearing. In the present work, a Head and Torso Simulator (HATS) has been used to capture the sound signal data from various conditions of fault in the bearing to mimic the natural hearing of human beings. After that, the acquired sound signal data was processed to get the Mel spectrogram of different bearing conditions and then put as input to a designed Convolutional Neural Network (CNN) for the intelligent diagnosis. The accuracy range of 97.11 % - 99.93 % is obtained in the case of left ear data, and this range for right ear data is 97.43 % - 99.97 %. The obtained results signify the robustness of the proposed method in the intelligent diagnosis of faults in the bearing at different speeds and load conditions." @default.
• W4385816158 created "2023-08-15" @default.
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• W4385816158 date "2023-03-24" @default.
• W4385816158 modified "2023-09-25" @default.
• W4385816158 title "Mel-spectrogram based Approach for Fault Detection in Ball Bearing using Convolutional Neural Network" @default.
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• W4385816158 doi "https://doi.org/10.1145/3592307.3592352" @default.
• W4385816158 hasPublicationYear "2023" @default.
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