Hejazi, Shahd
2024.
Improving Induction Motor fault classification accuracy through enhanced multimodal preprocessing, artificial image synthesis, deep learning and load-adaptive graph-based methods.
PhD Thesis,
Cardiff University.
Item availability restricted. |
|
|
Hejazi, Shahd, Packianather, Michael ORCID: https://orcid.org/0000-0002-9436-8206 and Liu, Y
2024.
Using DCGAN and WGAN-GP to generate artificial thermal RGB images for induction motors.
Presented at: Cardiff University Engineering Research Conference 2023,
Cardiff, UK,
12-14 July 2023.
Published in: Spezi, Emiliano and Bray, Michaela eds.
Proceedings of the Cardiff University Engineering Research Conference 2023.
Cardiff:
Cardiff University Press,
pp. 113-117.
10.18573/conf1.aa
|
|
|
Hejazi, Shahd, Packianather, Michael ORCID: https://orcid.org/0000-0002-9436-8206 and Liu, Ying ORCID: https://orcid.org/0000-0001-9319-5940
2023.
A Novel approach using WGAN-GP and conditional WGAN-GP for generating artificial thermal images of induction motor faults.
Presented at: 27th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES 2023),
06-08 September 2023.
Procedia Computer Science.
, vol.225
Elsevier,
pp. 3681-3691.
10.1016/j.procs.2023.10.363
|
|
|
Hejazi, Shahd, Packianather, Michael ORCID: https://orcid.org/0000-0002-9436-8206 and Liu, Ying ORCID: https://orcid.org/0000-0001-9319-5940
2022.
Novel preprocessing of multimodal condition monitoring data for classifying induction motor faults using deep learning methods.
Presented at: IEEE 2nd International Symposium on Sustainable Energy, Signal Processing and Cyber Security (iSSSC),
Gunupur, Odisha, India,
15-17 December 2022.
Proceedings of 2nd International Symposium on Sustainable Energy, Signal Processing and Cyber Security.
IEEE,
pp. 1-6.
10.1109/iSSSC56467.2022.10051321
|
|



Up a level