Bearing fault diagnosis using lightweight and robust one-dimensional convolution neural network in the frequency domain

• Main Authors: Hakim, Mohammed, Omran, Abdoulhadi A. Borhana, Inayat-Hussain, Jawaid I., Ahmed, Ali Najah, Abdellatef, Hamdan, Abdellatif, Abdallah, Gheni, Hassan Muwafaq
• Format: Article
• Published: MDPI 2022
• Subjects: TK Electrical engineering. Electronics Nuclear engineering
• Online Access: http://eprints.um.edu.my/41558/

The massive environmental noise interference and insufficient effective sample degradation data of the intelligent fault diagnosis performance methods pose an extremely concerning issue. Realising the challenge of developing a facile and straightforward model that resolves these problems, this study...