Wavelet packet decomposition with motif patterns for rolling bearing fault diagnosis under variable working loads

Wang, Qiang; Xu, Feiyun; Ma, Tianchi

doi:10.1177/10775463241229512

Cited by 2 publications

(1 citation statement)

References 31 publications

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“…Rolling bearings are crucial components in rotating machinery, significantly influencing equipment reliability and stability [1]. However, their prolonged exposure to complex working environments makes them susceptible to malfunctions, which can negatively impact machine performance and pose safety risks [2,3]. Effective fault diagnosis techniques for rolling bearings are therefore essential [4,5].…”

Section: Introductionmentioning

confidence: 99%

Bearing Fault Vibration Signal Denoising Based on Adaptive Denoising Autoencoder

Lu,

Zhou,

2024

Electronics

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Vibration signal analysis is regarded as a fundamental approach in diagnosing faults in rolling bearings, and recent advancements have shown notable progress in this domain. However, the presence of substantial background noise often results in the masking of these fault signals, posing a significant challenge for researchers. In response, an adaptive denoising autoencoder (ADAE) approach is proposed in this paper. The data representations are learned by the encoder through convolutional layers, while the data reconstruction is performed by the decoder using deconvolutional layers. Both the encoder and decoder incorporate adaptive shrinkage units to simulate denoising functions, effectively removing interfering information while preserving sensitive fault features. Additionally, dropout regularization is applied to sparsify the network and prevent overfitting, thereby enhancing the overall expressive power of the model. To further enhance ADAE’s noise resistance, shortcut connections are added. Evaluation using publicly available datasets under scenarios with known and unknown noise demonstrates that ADAE effectively enhances the signal-to-noise ratio in strongly noisy backgrounds, facilitating accurate diagnosis of faults in rolling bearings.

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Section: Introductionmentioning

confidence: 99%

Bearing Fault Vibration Signal Denoising Based on Adaptive Denoising Autoencoder

Lu,

Zhou,

2024

Electronics

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Multilayer Fused Correntropy Reprsenstation for Fault Diagnosis of Mechanical Equipment

Deng,

Zhao,

Jiang

et al. 2024

Sensors

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Fault diagnosis is vital for improving the reliability and safety of mechanical equipment. Existing fault diagnosis methods require a large number of samples for model training. However, in real-world environments, mechanical equipment usually operates under healthy conditions during most of its service life, resulting in a scarcity of fault samples. To solve this problem, a novel multilayer fusion correntropy representation method combined with a support vector machine is proposed for the fault diagnosis of mechanical equipment. First, the monitoring signal is expanded into multilayer signal components using wavelet packet decomposition. Then, the correlation between the signal components of each layer is expressed by correntropy, and the corresponding correntropy matrix is constructed. After performing the matrix logarithm operator, all correntropy matrices composed of correntropy values are fused into a vector, which is viewed as a feature of the signal. Finally, a support vector machine is established using small samples to realize fault classification. The effectiveness of the proposed method is validated on four public datasets. The results indicate that compared with other methods, the proposed method has advantages in terms of diagnosis accuracy and noise immunity ability.

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Wavelet packet decomposition with motif patterns for rolling bearing fault diagnosis under variable working loads

Cited by 2 publications

References 31 publications

Bearing Fault Vibration Signal Denoising Based on Adaptive Denoising Autoencoder

Bearing Fault Vibration Signal Denoising Based on Adaptive Denoising Autoencoder

Multilayer Fused Correntropy Reprsenstation for Fault Diagnosis of Mechanical Equipment

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