Denoising diffusion implicit model for bearing fault diagnosis under different working loads

• Published in: ITM Web of Conferences Vol. 63; p. 1025
• Main Authors: Wong, Toong Yang, Lim, Meng Hee, Ngui, Wai Keng, Salman Leong, Mohd
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Les Ulis EDP Sciences 2024
• Subjects: Artificial neural networks; Classification; Damage prevention; Datasets; Deep learning; Fault diagnosis; Fourier transforms; Hilbert transformation; Machine learning; Model accuracy; Neural networks; Noise reduction; Performance enhancement; Rotating machinery; Synthetic data; Time-frequency analysis; Vibration analysis; Wavelet transforms
• ISSN: 2271-2097; 2431-7578; 2271-2097
• DOI: 10.1051/itmconf/20246301025

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.