Improved SE-ResNet Acoustic–Vibration Fusion for Rolling Bearing Composite Fault Diagnosis

Gu, Xiaojiao; Tian, Yang; Li, Chi; Wei, Yonghe; Li, Dashuai

doi:10.3390/app14052182

Cited by 5 publications

(1 citation statement)

References 26 publications

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“…CNNs excel in learning hierarchical representations, which are vital for distinguishing fault features from noise [24][25][26]. Studies have demonstrated that CNN-based models outperform traditional methods in both accuracy and robustness under varying noise conditions [27][28][29]. These models can capture complex patterns and interactions within the data that are often missed by traditional techniques.…”

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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Intelligent Fault Diagnosis of Rolling Bearings Based on Markov Transition Field and Mixed Attention Residual Network

Tong,

Zhang,

Wang

et al. 2024

Applied Sciences

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To address the problems of existing methods that struggle to effectively extract fault features and unstable model training using unbalanced data, this paper proposes a new fault diagnosis method for rolling bearings based on a Markov Transition Field (MTF) and Mixed Attention Residual Network (MARN). The acquired vibration signals are transformed into two-dimensional MTF feature images as network inputs to avoid the loss of the original signal information, while retaining the temporal correlation; then, the mixed attention mechanism is inserted into the residual structure to enhance the feature extraction capability, and finally, the network is trained and outputs diagnostic results. In order to validate the feasibility of the MARN, other popular deep learning (DL) methods are compared on balanced and unbalanced datasets divided by a CWRU fault bearing dataset, and the proposed method results in superior performance. Ultimately, the proposed method achieves an average recognition accuracy of 99.5% and 99.2% under the two categories of divided datasets, respectively.

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Combined ResNet Attention Multi-Head Net (CRAMNet): A Novel Approach to Fault Diagnosis of Rolling Bearings Using Acoustic Radiation Signals and Advanced Deep Learning Techniques

Xu,

Li,

Ding

2024

Applied Sciences

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The fault diagnosis of rolling bearing acoustic radiation signals holds significant importance in industrial equipment maintenance. It effectively prevents equipment failures and downtime, ensuring the smooth operation of the production process. Compared with traditional vibration signals, acoustic radiation signals have the advantage of non-contact measurement. They can diagnose faults in special conditions where sensors cannot be installed and provide more comprehensive equipment status information. Therefore, to extract the fault characteristic information of rolling bearings from complex acoustic signals, this paper proposes an advanced deep learning model combining Gramian Angular Field (GAF), ResNet1D, ResNet2D, and multi-head attention mechanism, named CRAMNet (Combined ResNet Attention Multi-Head Net), to diagnose the faults of rolling bearing acoustic radiation signals. Firstly, this method includes converting one-dimensional signals into GAF images and performing data standardization and segmentation. Then, the method utilizes ResNet1D to extract features from one-dimensional signals and ResNet2D to extract features from GAF images. Further, it combines the multi-head attention mechanism to enhance feature representation and capture dependencies between different channels. Finally, this paper compares the proposed method with several traditional models (including CNN, LSTM, DenseNet, and CNN-Transformers). Experimental results show that the proposed method performs outstandingly in terms of accuracy and robustness. The combination of residual networks and multi-head attention mechanism in the model significantly enhances its ability to accurately diagnose rolling bearing faults, proving the superiority of the algorithm.

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Improved SE-ResNet Acoustic–Vibration Fusion for Rolling Bearing Composite Fault Diagnosis

Cited by 5 publications

References 26 publications

Bearing Fault Vibration Signal Denoising Based on Adaptive Denoising Autoencoder

Bearing Fault Vibration Signal Denoising Based on Adaptive Denoising Autoencoder

Intelligent Fault Diagnosis of Rolling Bearings Based on Markov Transition Field and Mixed Attention Residual Network

Combined ResNet Attention Multi-Head Net (CRAMNet): A Novel Approach to Fault Diagnosis of Rolling Bearings Using Acoustic Radiation Signals and Advanced Deep Learning Techniques

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