Research on Fault Diagnosis of Highway Bi-LSTM Based on Attention Mechanism

Li, Xueyi; Su, Kaiyu; He, Qiushi; Wang, Xiangkai; Xie, Zhijie

doi:10.17531/ein/162937

Cited by 8 publications

(5 citation statements)

References 31 publications

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“…To demonstrate the superiority of our proposed model, we conducted comparisons with the Bi-LSTM [36], CNN_1D [37], and swin transformer (Swin_T) [38]. As shown in the figure 9, we plotted the ROC curves for each method on the CWRU dataset, and the area under the curve indicates that the transfer learning-based bearing diagnosis accuracy of our proposed method is higher than that of other models.…”

Section: Case Western Reserve University (Cwru) Bearing Dataset Exper...mentioning

confidence: 99%

Transfer learning for bearing fault diagnosis: adaptive batch normalization and combined optimization method

Li,

Su,

et al. 2024

Meas. Sci. Technol.

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Bearings are crucial components in rotating machinery equipment. Bearing fault diagnosis plays a significant role in the maintenance of mechanical equipment. In practical industrial settings, equipment conditions often vary continuously, making it challenging to collect data for all operating conditions for bearing fault diagnosis. This paper proposes a transfer learning approach for bearing fault diagnosis based on Adaptive Batch Normalization (AdaBN) and a combined optimization algorithm. Initially, a ResNet neural network is trained using source domain data. Subsequently, the trained model is transferred to the target domain, where AdaBN is applied to mitigate domain shift issues. Furthermore, a combined optimization algorithm is employed during model training to enhance fault diagnosis accuracy. Experimental validation is conducted using bearing data from the CWRU dataset and NEFU dataset. Comparison shows that AdaBN and the combined optimization algorithm improve bearing fault diagnosis accuracy effectively. On the NEFU dataset, the diagnostic accuracy exceeds 95%.

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Section: Case Western Reserve University (Cwru) Bearing Dataset Exper...mentioning

confidence: 99%

Transfer learning for bearing fault diagnosis: adaptive batch normalization and combined optimization method

Li,

Su,

et al. 2024

Meas. Sci. Technol.

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“…where 𝜎 is the sigmoid activation function, ℎ is the hidden layer state vector at time t-1, 𝑥 is the input vector at time t, 𝑏 is the bias of the corresponding gate, and 𝑤 is the weight of the corresponding gate [5] .…”

Section: Long Short-term Memory(lstm)mentioning

confidence: 99%

Chemical Process Fault Diagnosis Method Based on Deep Learning

Zhang,

2023

J. Phys.: Conf. Ser.

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To address the problem that the traditional fault diagnosis method for chemical processes under big data relies too much on expert experience and fault features are difficult to distinguish, a deep learning-based fault diagnosis method is proposed, which combines convolutional neural network (CNN), long and short-term memory (LSTM) and attention mechanism (AM). In this method, the spatial sequence features of the input signal are extracted by the CNN adaptively, while the LSTM extracts the time-series features of the signal. Finally, the model performance is enhanced by introducing the attention mechanism and using the SoftMax layer as a classifier for fault diagnosis, so that the model can notice the important features of the faults with the interference of noise. Simulation validation of the method in this paper is performed using the TE chemical process data set, and it is demonstrated that the method can be used for chemical process fault diagnosis studies. Finally, compared with other fault diagnosis methods, the method is more accurate and has certain superiority.

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“…Many experiments have demonstrated that the BN can significantly reduce the number of iterations while improving the final model performance. The BN is already a necessary part of many top-level architectures such as ResNet [34] and Inception V3 [35].…”

Section: Batch Normalizationmentioning

confidence: 99%

A Migration Learning Method Based on Adaptive Batch Normalization Improved Rotating Machinery Fault Diagnosis

et al. 2023

Sustainability

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Sustainable development has become increasingly important as one of the key research directions for the future. In the field of rotating machinery, stable operation and sustainable performance are critical, focusing on the fault diagnosis of component bearings. However, traditional normalization methods are ineffective in target domain data due to the difference in data distribution between the source and target domains. To overcome this issue, this paper proposes a bearing fault diagnosis method based on the adaptive batch normalization algorithm, which aims to enhance the generalization ability of the model in different data distributions and environments. The adaptive batch normalization algorithm improves the adaptability and generalization ability to better respond to changes in data distribution and the real-time requirements of practical applications. This algorithm replaces the statistical values in a BN with domain adaptive mean and variance statistics to minimize feature differences between two different domains. Experimental results show that the proposed method outperforms other methods in terms of performance and generalization ability, effectively solving the problems of data distribution changes and real-time requirements in bearing fault diagnosis. The research results indicate that the adaptive batch normalization algorithm is a feasible method to improve the accuracy and reliability of bearing fault diagnosis.

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Research on Fault Diagnosis of Highway Bi-LSTM Based on Attention Mechanism

Cited by 8 publications

References 31 publications

Transfer learning for bearing fault diagnosis: adaptive batch normalization and combined optimization method

Transfer learning for bearing fault diagnosis: adaptive batch normalization and combined optimization method

Chemical Process Fault Diagnosis Method Based on Deep Learning

A Migration Learning Method Based on Adaptive Batch Normalization Improved Rotating Machinery Fault Diagnosis

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