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Communication Dans Un Congrès Année : 2016

Gear Fault Diagnosis Using Discrete Wavelet Transform and Deep Neural Networks

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Résumé

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.
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Dates et versions

hal-03629908 , version 1 (04-04-2022)

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  • HAL Id : hal-03629908 , version 1

Citer

Mehrdad Heydarzadeh, Shahin Hedayati Kia, Mehrdad Nourani, Humberto Henao, Gerard-Andre Capolino. Gear Fault Diagnosis Using Discrete Wavelet Transform and Deep Neural Networks. PROCEEDINGS OF THE IECON 2016 - 42ND ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY, Oct 2016, Florence, Italy. pp.1494-1500. ⟨hal-03629908⟩

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