Improved signal processing for bearing fault diagnosis in noisy environments using signal denoising, time–frequency transform, and deep learning

Hamdaoui, Hind; Ngiejungbwen, Looh Augustine; Gu, Jinan; Tang, Shixi

doi:10.1007/s40430-023-04471-9

Cited by 4 publications

(1 citation statement)

References 66 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Traditionally, the fault diagnosis method of rolling bearings mainly determines the bearing failure by signal processing of raw vibration signals, which has been the mainstream diagnostic method in recent years [5][6][7][8][9]. Commonly used signal processing methods can analyze and decompose vibration signals in the time, frequency and time-frequency domains, such as the fast Fourier transform [10], the shorttime Fourier transform [11], the continuous wavelet transform (CWT) [12], the empirical wavelet transform [13], the wavelet packet transform [14], and empirical modal decomposition [15].…”

Section: Introductionmentioning

confidence: 99%

Research on bearing fault diagnosis based on novel MRSVD-CWT and improved CNN-LSTM

Guo,

Zhou,

Dong

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

As a critical component in mechanical equipment, rolling bearings play a vital role in industrial production. Effective bearing fault diagnosis provides a more reliable guarantee for the safe operation of the industrial output. Traditional data-driven bearing fault diagnosis methods often have problems such as insufficient fault feature extraction and poor model generalization capabilities, resulting in reduced diagnostic accuracy. To solve these problems and significantly improve the diagnosis accuracy, this paper proposes a novel fault diagnosis method based on multi-resolution singular value decomposition (MRSVD), continuous wavelet transform (CWT), improved convolutional neural network (CNN) enhanced by convolutional block attention module (CBAM), and long short-term memory (LSTM). Through MRSVD, the vibration signal is decomposed layer by layer into multiple denoised signals, thus signal noise can be eliminated to the greatest extent to gain the optimal denoised signals; then through CWT, the optimal denoised signals are converted into two-dimensional time-frequency images so that the local and global characteristic information can be fully captured. Finally, through improved CNN-LSTM, feature extraction is greatly enhanced, resulting in high accuracy of fault diagnosis. Lots of experiments are organized to test the performance, and the experimental results show that the proposed method on various datasets has better diagnosis accuracy and generalization ability under different working conditions than other methods.

show abstract