Modern Methods for Signal Analysis and Its Applications

Kumar, Sachin; Mohan, Neethu; Poornachandran, Prabaharan

doi:10.1007/978-3-030-12500-4_17

Cited by 7 publications

(1 citation statement)

References 21 publications

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“…Vibration signals can be used directly as time series data or two-dimensional images [2]. Common time-frequency analysis used to transform time series vibration signal into two-dimensional images are short-time Fourier Transform (STFT), continuous wavelet transform (CWT), and Hilbert Huang Transform (HHT) [3]. CNN model is usually used for image classification.…”

Section: Introductionmentioning

confidence: 99%

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

Wong,

Lim,

Ngui

et al. 2024

ITM Web Conf.

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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.

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Section: Introductionmentioning

confidence: 99%