Mechanical and electrical faults detection in induction motor across multiple sensors with CNN-LSTM deep learning model
- 3 May 2024
- journal article
- research article
- Published by Springer Nature in Electrical Engineering
- Vol. 106 (6) , 6941-6951
Abstract
No abstract availableKeywords
This publication has 29 references indexed in Scilit:
- University of Ottawa constant and variable speed electric motor vibration and acoustic fault signature datasetData in Brief, 2024
- Real-time robust bearing fault detection using scattergram-driven hybrid CNN-SVMElectrical Engineering, 2023
- Harnessing attention mechanisms in a comprehensive deep learning approach for induction motor fault diagnosis using raw electrical signalsEngineering Applications of Artificial Intelligence, 2023
- A hybrid method based on deep learning and ensemble learning for induction motor fault detection using sound signalsMultimedia Tools and Applications, 2023
- Thermographic fault diagnosis of electrical faults of commutator and induction motorsEngineering Applications of Artificial Intelligence, 2023
- Multi-input CNN based vibro-acoustic fusion for accurate fault diagnosis of induction motorEngineering Applications of Artificial Intelligence, 2023
- A systematic review of rolling bearing fault diagnoses based on deep learning and transfer learning: Taxonomy, overview, application, open challenges, weaknesses and recommendationsAin Shams Engineering Journal, 2022
- A physics-informed deep learning approach for bearing fault detectionEngineering Applications of Artificial Intelligence, 2021
- Enhanced bearing fault detection using multichannel, multilevel 1D CNN classifierElectrical Engineering, 2021
- End-to-end CNN + LSTM deep learning approach for bearing fault diagnosisApplied Intelligence, 2020