A dual attention mechanism network with self-attention and frequency channel attention for intelligent diagnosis of multiple rolling bearing fault types

Zhang, Wenxing; Yang, Jianhong; Bo, Xinyu; Yang, Zhenkai

doi:10.1088/1361-6501/ad1811

Cited by 6 publications

(3 citation statements)

References 44 publications

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“…In order to capture the key features, the attention mechanism [25] is used to process the global feature vector of the BiLSTM network. The calculation rule is as follows:…”

Section: Mtl With Lstm Networkmentioning

confidence: 99%

Domain-alignment multitask learning network for partial discharge condition assessment with digital twin in gas-insulated switchgear

Yan,

Wang,

Zhang

et al. 2024

Meas. Sci. Technol.

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Deep-learning-driven methods have made great progress in the condition assessment of partial discharge (PD) which including diagnosis and location in gas-insulated switchgear (GIS). However, these methods perform diagnosis and location as two separate tasks and ignore the coupling relationship. In addition, these methods all require obtaining sufficient samples to develop models, and the model becomes ineffective when there is a significant difference in sample distribution. Therefore, we propose a novel domain-alignment multitask learning network (DAMTLN) for condition assessment including diagnosis and location on-site assisted by digital twin. Firstly, a digital virtual model is established to assist the actual condition assessment ofGIS PD. Then, a novel multitask network is constructed to mine the coupling relationship between the two tasks. Finally PD condition assessment guided by a digital twin model are achieved via a combination of local-maximummean-discrepancy-based and adversarial -based domain adaptation, in which fine-grained information on each category is captured. Experimental results show that the proposed DAMTLN achieved a diagnostic accuracy of98.725%, and the mean absolute error of location was 9.055 cm, which were significantly better than the results of other methods. The DAMTLN thus provides a new avenue for PD diagnosis and location driven by “data– physics” coupling..

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“…In order to capture the key features, the attention mechanism [25] is used to process the global feature vector of the BiLSTM network. The calculation rule is as follows:…”

Section: Mtl With Lstm Networkmentioning

confidence: 99%

Domain-alignment multitask learning network for partial discharge condition assessment with digital twin in gas-insulated switchgear

Yan,

Wang,

Zhang

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…These buzzwords last for four to seven years. The 2021-2024 buzzwords include 'convolutional neural networks [53], deep learning, data models, transfer learning [54], and the use of artificial neural networks [55]', etc. These salient terms reflect the rise of deep learning techniques in the field of fault diagnosis, particularly the novel algorithms represented by transfer learning.…”

Section: Analysis Of High-yield Institutionsmentioning

confidence: 99%

Analysis of hot spots and trends in rolling bearing fault diagnosis research based on scientific knowledge mapping

Chen,

Zhao,

Zhang

et al. 2024

Eng. Res. Express

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As a key component of mechanical equipment, real-time monitoring and diagnosis of rolling bearings play a critical role in ensuring the stable operation of equipment and the safety of operators. In order to present the current status and trends of fault diagnosis research on rolling bearings more intuitively, the scientific knowledge mapping was used to visualize and analyze the relevant literature in the article. The results show that the number of publications in this area of research has grown significantly in recent years, with China, India, the United States, and England having contributed significantly. The journals such as MECHANICAL SYSTEMS AND SIGNAL PROCESSING, MEASUREMENT, and JOURNAL OF SOUND AND VIBRATION have played an important role in disseminating cutting-edge technologies in this field. In addition, the exploration of modern methods based on data-driven and artificial intelligence, as well as their application to real-world problems, are gradually becoming the focus of research. Through summarising and analysing, the application of modern data processing techniques, the development of more efficient and practical intelligent fault diagnosis techniques, and the close integration of laboratory research and practical applications will become future research trends.

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“…The advancement of deep learning has facilitated its extensive application in FD [32], with numerous models based on neural network such as CNN and RNN [33], bypassing the need for expert knowledge required by traditional methods [34]. These models extract rich features from the original signals for direct classification diagnosis.…”

Section: Deep Learning In Fdmentioning

confidence: 99%

Multi-scale quaternion CNN and BiGRU with cross self-attention feature fusion for fault diagnosis of bearing

Liu,

Zhang,

Tan

et al. 2024

Meas. Sci. Technol.

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In recent years, deep learning has led to significant advances in bearing fault diagnosis (FD). Most techniques aim to achieve greater accuracy. However, they are sensitive to noise and lack robustness, resulting in insufficient domain adaptation and anti-noise ability. The comparison of studies reveals that giving equal attention to all features does not differentiate their significance. In this work, we propose a novel FD model by integrating multi-scale quaternion convolutional neural network (MQCNN), bidirectional gated recurrent unit (BiGRU), and cross self-attention feature fusion (CSAFF). We have developed innovative designs in two modules, namely MQCNN and CSAFF. Firstly, MQCNN applies quaternion convolution to multi-scale architecture for the first time, aiming to extract the rich hidden features of the original signal from multiple scales. Then, the extracted multi-scale information is input into CSAFF for feature fusion, where CSAFF innovatively incorporates cross self-attention mechanism to enhance discriminative interaction representation within features. Finally, BiGRU captures temporal dependencies while a softmax layer is employed for fault classification, achieving accurate FD. To assess the efficacy of our approach, we experiment on three public datasets (CWRU, MFPT, and Ottawa) and compare it with other excellent methods. The results confirm its state-of-the-art, which the average accuracies can achieve up to 99.99%, 100%, and 99.21% on CWRU, MFPT, and Ottawa datasets. Moreover, we perform practical tests and ablation experiments to validate the efficacy and robustness of the proposed approach.

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A dual attention mechanism network with self-attention and frequency channel attention for intelligent diagnosis of multiple rolling bearing fault types

Cited by 6 publications

References 44 publications

Domain-alignment multitask learning network for partial discharge condition assessment with digital twin in gas-insulated switchgear

Domain-alignment multitask learning network for partial discharge condition assessment with digital twin in gas-insulated switchgear

Analysis of hot spots and trends in rolling bearing fault diagnosis research based on scientific knowledge mapping

Multi-scale quaternion CNN and BiGRU with cross self-attention feature fusion for fault diagnosis of bearing

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