Exploring evolutionary-tuned autoencoder-based architectures for fault diagnosis in a wind turbine gearbox

Samuel M. Gbashia Department of Mechanical Engineering Science, University of Johannesburg, Johannesburg, South AfricaCorrespondence[email protected]

https://orcid.org/0000-0001-9393-6091 View further author information

Obafemi O. Olatunjia Department of Mechanical Engineering Science, University of Johannesburg, Johannesburg, South Africa;b Process Energy and Environmental Technology Station (PEETS), University of Johannesburg, Johannesburg, South AfricaView further author information

Paul A. Adedejia Department of Mechanical Engineering Science, University of Johannesburg, Johannesburg, South Africa;b Process Energy and Environmental Technology Station (PEETS), University of Johannesburg, Johannesburg, South AfricaView further author information

Nkosinathi Madushelea Department of Mechanical Engineering Science, University of Johannesburg, Johannesburg, South Africa;c Department of Mechanical Engineering, Durban University of Technology, Durban, South AfricaCorrespondence[email protected]
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Exploring evolutionary-tuned autoencoder-based architectures for fault diagnosis in a wind turbine gearbox

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Exploring evolutionary-tuned autoencoder-based architectures for fault diagnosis in a wind turbine gearbox

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