Wong, Toong Yang and Lim, Meng Hee and Ngui, Wai Keng and Leong, Mohd Salman (2024) Denoising diffusion implicit model for bearing fault diagnosis under different working loads. In: ITM Web Conf.. 1st International Conference on Advances in Machine Intelligence, and Cybersecurity Technologies (AMICT2023) , 11-12 December 2023 , Virtual, Online. pp. 1-8., 63 (01025). ISSN 2271-2097
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Abstract
Rotating machineries always operating under different loads and suffer from various types of bearing fault. Thus, bearing fault diagnosis is essential to prevent further loss or damage. Deep learning has been favoured over machine learning recently due to data explosion and its higher performance. In deep learning-based bearing fault diagnosis, vibration signals are usually transformed into images using time frequency analysis methods such as short-time Fourier transform, wavelet transform, and Hilbert-Huang transform. Convolutional neural network (CNN) is widely used for fault classification method. However, the training dataset and testing dataset usually have different load domains due to different working conditions. Obtaining training data of wide range of loadings are impractical and exhausting. Thus, this study is proposed to solve load domain adaptation using denoising diffusion implicit model (DDIM). In this study, synthetic images are generated using DDIM model while only convolutional neural network (CNN) is used as fault classification model. The classification accuracy of testing dataset is obtained using CNN models trained with original training dataset and augmented training dataset. The results showed that the synthetic scalograms could improve the performance of CNN model by 3.3% under different load domains.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Uncontrolled Keywords: | Convolutional neural network (CNN), CNN; dataset; Rotating machineries |
| Subjects: | T Technology > TJ Mechanical engineering and machinery T Technology > TL Motor vehicles. Aeronautics. Astronautics |
| Faculty/Division: | Faculty of Mechanical and Automotive Engineering Technology |
| Depositing User: | Miss Amelia Binti Hasan |
| Date Deposited: | 28 Jun 2024 08:07 |
| Last Modified: | 28 Jun 2024 08:07 |
| URI: | http://umpir.ump.edu.my/id/eprint/41735 |
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