Szczegóły publikacji
Opis bibliograficzny
Advanced fault diagnosis in rotating machines using 2D grayscale images with improved Deep Convolutional Neural Networks / Muhammad Ahsan, Dariusz Bismor // W: SPA 2023 : Signal Processing Algorithms, Architectures, Arrangements, and Applications : Poznan, 20th - 22nd September 2023 / IEEE The Institute of Electrical and Electronics Engineers Inc., [etc.]. — [Piscataway] : IEEE, [2023]. — ( Signal Processing Algorithms, Architectures, Arrangements, and Applications Conference Proceedings ; ISSN 2326-0262 ). — ISBN: 979-8-3503-0498-5. — S. 77–82. — Bibliogr. s. 81–82, Abstr. — Publikacja dostępna online od: 2023-10-10. — M. Ahsan - afiliacja: Department of Measurements and Control Systems Silesian University of Technology, Gliwice, Poland
Autorzy (2)
- Ahsan Muhammad
- Bismor Dariusz
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 165526 |
|---|---|
| Data dodania do BaDAP | 2026-01-21 |
| Tekst źródłowy | URL |
| DOI | 10.23919/SPA59660.2023.10274428 |
| Rok publikacji | 2023 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Institute of Electrical and Electronics Engineers (IEEE) |
| Czasopismo/seria | Signal Processing Algorithms, Architectures, Arrangements, and Applications Conference Proceedings |
Abstract
This paper presents a Deep Convolutional Neural Network (DCNN) model with a SoftMax classifier for the diagnosis of bearing and gear faults based on 2D grayscale images converted from raw vibration signals. The proposed model is validated and verified using a vibration dataset consisting of gear and bearing vibrations recorded under different operating conditions and fault conditions. The experimental results demonstrate that the DCNN achieves remarkable accuracies for both bearing fault diagnosis under different operating conditions and fault conditions, with high validation accuracy achieved. Furthermore, the performance of the proposed model is evaluated and compared with existing models in the literature for the same datasets, and it is concluded that the proposed DCNN is more efficient in terms of fault diagnosis accuracy, demonstrating its superiority. Therefore, the proposed high-accuracy DCNN can be an effective and accurate tool for fault diagnosis in various industrial applications. © 2023 Division of Signal Processing and Electronic Systems, Poznan University of Technology (DSPES PUT).
