Multi-sensor signals with parallel attention convolutional neural network for bearing fault diagnosis

Xing, Zhikai; Liu, Yongbao; Wang, Qiang; Li, Jun

doi:10.1063/5.0095530

Cited by 10 publications

(7 citation statements)

References 43 publications

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“…The proposed model is compared with three multi-sensor fusion based fault diagnosis methods, including DCNN [14], LSTM [6], MS-PACNN [4], TSCNN [5], and Conv-LSTM [8]. To evaluate the anti-noise performance of different models, we add random Gaussian white noise with different SNRs to each data channel of different sensors.…”

Section: Comparisons Of Different Methodsmentioning

confidence: 99%

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Adaptive wavelet pyramid for noisy machinery fault diagnosis with multiple sensors

Tian

et al. 2022

Electronics Letters

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The fusion of multiple monitoring sensors is crucial to improve the accuracy and robustness of machinery fault diagnosis. However, existing fault diagnosis methods may underestimate the interference of noise in the multi‐sensor fusion process, leading to unsatisfied performance. To handle this problem, this paper proposes a deep model based on the frequency adaptive wavelet pyramid. First, an adaptive frequency selection strategy is designed to prune the seriously polluted frequencies and only retain some key frequencies. Then, the self‐attention mechanism is used to perform information fusion on the selected frequency bands of different sensors. Finally, a wavelet fusion pyramid is adopted by repeating the fusion process at multiple wavelet decomposition levels. In this way, different sensors can be fused in a more fine‐grained manner. The experimental results on two multi‐sensor‐based fault diagnosis datasets demonstrate the anti‐noise capability of the proposed method.

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Section: Comparisons Of Different Methodsmentioning

confidence: 99%

“…In ref. [4], a multi‐branch CNN was designed to extract temporal and spatial features from different sensors, respectively. Then, the attention mechanism was applied to enhance the features of important sensors.…”

Section: Introductionmentioning

confidence: 99%

Adaptive wavelet pyramid for noisy machinery fault diagnosis with multiple sensors

Tian

et al. 2022

Electronics Letters

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“…In [5], a 1D-CNNbased network was proposed to fuse the concatenated multi-sensor data for fault diagnosis. In [6], a multi-branch CNN was designed to extract temporal and spatial features from different sensors, respectively. Then, the attention mechanism was applied to enhance the features of important sensors.…”

Section: Hosted Filementioning

confidence: 99%

“…Comparisons of Different Methods: The proposed model is compared with three multi-sensor fusion based fault diagnosis methods, including DCNN [5], LSTM [7] and MS-PACNN [6]. To evaluate the anti-noise performance of different models, we add random Gaussian white noise with different SNRs to each data channel of different sensors.…”

Section: Performance Evaluationmentioning

confidence: 99%

Frequency adaptive wavelet pyramid for noisy machinery fault diagnosis with multiple sensors

Tian

Zhang

et al. 2022

Preprint

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The fusion of multiple monitoring sensors is crucial to improve the accuracy and robustness of machinery fault diagnosis. However, existing fault diagnosis methods may underestimate the interference of noise in the multi-sensor fusion process, leading to unsatisfied performance. To handle this problem, this paper proposes a deep model based on the frequency adaptive wavelet pyramid. First, an adaptive frequency selection strategy is designed to prune the seriously polluted frequencies and only retain some key frequencies. Then, the self-attention mechanism is used to perform information fusion on the selected frequency bands of different sensors. Finally, a wavelet fusion pyramid is adopted by repeating the fusion process at multiple wavelet decomposition levels. In this way, different sensors can be fused in a more fine-grained manner. The experimental results on two multi-sensor-based fault diagnosis datasets demonstrate the anti-noise capability of our proposed method.

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“…For example, Xu et al 34 took an integrated model based on CNN, deep residual network (DRN), LSTM multi-model parallelism, and multi-sensor feature fusion, which has good robustness and accuracy. Xing et al 35 combined multi-sensor signals with the feature layer fusion model of the parallel attention CNN. Duan et al 36 used a deep focusing parallel convolutional neural network (DFPCN) learning method to overcome data imbalance and achieve higher accuracy and stability.…”

Section: Introductionmentioning

confidence: 99%

A fault diagnosis method based on improved parallel convolutional neural network for rolling bearing

Lin³

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part G:

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There are many disadvantages for traditional Convolutional Neural Network (CNN) in rolling bearing fault diagnosis, such as low efficiency, weak noise immunity, and poor generalization with changing load. To solve the problem, this paper proposes a methodology of improved parallel CNN (IPCNN). In IPCNN, the simple pooling layer is removed and the parallel structure is to stack directly convolutional layers, with three branches, each branch has 4 layers, where the convolution kernels are all 3 × 3 and the stride sizes are 1, 2, and 3, respectively. The structure that is capable of feature fusion can extract features from the input information. Subsequently, the global average pooling (GAP) layer is used for down sampling, and the bearing faults are classified by the fully connected (FC) layer. In addition, the effectiveness of the proposed model structure is verified by testing the datasets. To further verify the validity of the model, the performance of the model was evaluated by diagnostic accuracy, prediction time, SD, and model size. In order to verify the noise immunity and generalization of the proposed model, the AlexNet, Vgg16, and ResNet18 models are compared, respectively. By performing 2D gray images transformation on the Case Western Reserve University (CWRU) bearing data, rolling bearing fault diagnosis method based on IPCNN model has higher efficiency, stronger noise resistance in the noise environment, and better generalization ability when the load changes.

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Multi-sensor signals with parallel attention convolutional neural network for bearing fault diagnosis

Cited by 10 publications

References 43 publications

Adaptive wavelet pyramid for noisy machinery fault diagnosis with multiple sensors

Adaptive wavelet pyramid for noisy machinery fault diagnosis with multiple sensors

Frequency adaptive wavelet pyramid for noisy machinery fault diagnosis with multiple sensors

A fault diagnosis method based on improved parallel convolutional neural network for rolling bearing

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