A Novel Fault Diagnosis Approach for Rolling Bearing Based on CWT and Adaptive Sparse Representation

Yuan, Xiong; Zhang, Huijie; Liu, Hui

doi:10.1155/2022/9079790

Cited by 2 publications

(2 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Generally, assuming that the input signal x(t) ∈ L 2 (R), the basic wavelet function in wavelet transform ψ(t) ∈ L 2 (R), where L 2 (R) expresses the square-integrable real number space, then the CWT [34] of the input signal x(t) can be expressed as:…”

Section: Cwtmentioning

confidence: 99%

Rolling bearing fault diagnosis based on 2D time-frequency images and data augmentation technique

Fu¹,

Jiang²,

Li³

et al. 2023

Meas. Sci. Technol.

View full text Add to dashboard Cite

It confronts great difficulty to apply the traditional rolling bearing fault diagnosis methods to adaptively extract features conducive to fault diagnosis under complex operating conditions, and obtaining numerous fault data under real operating conditions is difficult and costly. To address this problem, a fault diagnosis method based on two-dimensional time-frequency images and data augmentation is proposed. To begin with, the original one-dimensional time series signal is converted into two-dimensional time-frequency images by continuous wavelet transform to obtain the input data suitable for two-dimensional convolutional neural network. Secondly, data augmentation technique is employed to expand labeled fault data. Finally, the generated and original fault data are served as training samples to train the fault diagnosis model based on convolutional neural networks. Experimental studies are conducted on standard and real-world datasets to validate the proposed method and demonstrate its superiority over the traditional methods in detecting bearing faults.

show abstract

Section: Cwtmentioning

confidence: 99%

Rolling bearing fault diagnosis based on 2D time-frequency images and data augmentation technique

Fu¹,

Jiang²,

Li³

et al. 2023

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…Yang et al [19] introduced a multi-scale feature fusion CNN with an attention mechanism for bearing fault diagnosis. Common methods used to convert one-dimensional vibration signals into two-dimensional images include continuous wavelet transform (CWT) [20], short-time Fourier transform (STFT) [21], and Gramian angular field (GAF) [22]. Among them, CWT and STFT are both time-frequency analysis methods, which transform signals from the time domain to the frequency domain for analysis.…”

Section: Introductionmentioning

confidence: 99%

Bearing Fault Diagnosis Based on Image Information Fusion and Vision Transformer Transfer Learning Model

Zhang,

Li,

Cai

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

In order to improve the accuracy of bearing fault diagnosis under a small sample, variable load, and noise conditions, a new fault diagnosis method based on an image information fusion and Vision Transformer (ViT) transfer learning model is proposed in this paper. Firstly, the method applies continuous wavelet transform (CWT), Gramian angular summation field (GASF), and Gramian angular difference field (GADF) to the time series data, and generates three grayscale images. Then, the generated three grayscale images are merged into an information fusion image (IFI) using image processing techniques. Finally, the obtained IFIs are fed into the advanced ViT model and trained based on transfer learning. In order to verify the effectiveness and superiority of the proposed method, the rolling bearing dataset from Case Western Reserve University (CWRU) is used to carry out experimental studies under different working conditions. Experimental results show that the method proposed in this paper is superior to other traditional methods in terms of accuracy, and the effect of ViT model based on transfer learning (TLViT) training is better than that of the Resnet50 model based on transfer learning training (TLResnet50) under variable loads and small sample conditions. In addition, the experimental results also prove that the IFI with multiple image information has better anti-noise ability than the single information image. Therefore, the method proposed in this paper can improve the accuracy of bearing fault diagnosis under small sample, variable load and noise conditions, and provide a new method for bearing fault diagnosis.

show abstract

A Novel Fault Diagnosis Approach for Rolling Bearing Based on CWT and Adaptive Sparse Representation

Cited by 2 publications

References 26 publications

Rolling bearing fault diagnosis based on 2D time-frequency images and data augmentation technique

Rolling bearing fault diagnosis based on 2D time-frequency images and data augmentation technique

Bearing Fault Diagnosis Based on Image Information Fusion and Vision Transformer Transfer Learning Model

Contact Info

Product

Resources

About