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• W2568757877 abstract "Automatic fault diagnosis is an inseparable part of today's electromechanical systems. Advanced signal processing and machine learning techniques are required to address variabilities and uncertainties associated with the monitoring signals. In this paper, deep neural networks are employed to diagnose five classes of gearbox faults applied to three common monitoring signals, i.e. vibration, acoustic and torque. Discrete wavelet transform is used to provide the initial features as the inputs of the network. A test-rig based on a 250W three-phase squirrel cage induction machine shaft connected to a single stage helical gear is built for validation of the proposed method. The experimental results indicate accurate fault diagnosis in various conditions such as different modalities, signal variabilities, and load conditions." @default.
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• W2568757877 date "2016-10-01" @default.
• W2568757877 modified "2023-10-16" @default.
• W2568757877 title "Gear fault diagnosis using discrete wavelet transform and deep neural networks" @default.
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• W2568757877 doi "https://doi.org/10.1109/iecon.2016.7793549" @default.
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