Bearing fault diagnosis based on high-confidence pseudo-labels and dual-view multi-adversarial sparse joint attention network under variable working conditions

Pan, Cailu; Shang, Zhiwu; Li, Wanxiang; Liu, Fei; Tang, Lutai

doi:10.1016/j.engappai.2024.108625

Engineering Applications of Artificial Intelligence

2024

DOI: 10.1016/j.engappai.2024.108625

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Bearing fault diagnosis based on high-confidence pseudo-labels and dual-view multi-adversarial sparse joint attention network under variable working conditions

Cailu Pan,

Zhiwu Shang,

Wanxiang Li

et al.

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Cited by 2 publications

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Open-set domain adaptive fault diagnosis based on supervised contrastive learning and a complementary weighted dual adversarial network

Pan,

Shang,

Tang

et al. 2025

Mechanical Systems and Signal Processing

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Open-set domain adaptive fault diagnosis based on supervised contrastive learning and a complementary weighted dual adversarial network

Pan,

Shang,

Tang

et al. 2025

Mechanical Systems and Signal Processing

View full text Add to dashboard Cite

MDSC-FSPPA-LCFF network for diagnosis of rolling bearing with multipoint fault

Huang,

Yan,

Liu

et al. 2024

Meas. Sci. Technol.

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Faults that occur in rolling bearings during operation are complex and variable. While extensive research has been conducted on compound faults involving multiple components, studies on multiple faults in single component are relatively scarce. However, the occurrence of multiple faults in single component is a common phenomenon. To address the issues of difficulty in feature extraction, numerous network parameters, and slow computational speed, a multi-scale dynamic snake convolution with fast spatial pyramid pooling attention (MDSC-FSPPA) and lightweight comprehensive feature fusion (LCFF) network is proposed for multipoint fault diagnosis of rolling bearings. Firstly, multi-scale shallow feature extraction (MSFE) module is applied to extract the features from the original signals. Then, dynamic snake convolution (DSC) with FSPPA module is used to refine these features deeply. Subsequently, LCFF module is employed to reduce network parameters while still fully extracting fault features. Additionally, fault identification is obtained through the softmax function. Finally, the t-distributed stochastic neighbor embedding (t-SNE) method is utilized to visually demonstrate the fault classification performance of the proposed method. The experimental evaluation conducted on bearing datasets indicates that the proposed network exhibits excellent performance of multipoint fault diagnosis in rolling bearings.

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Bearing fault diagnosis based on high-confidence pseudo-labels and dual-view multi-adversarial sparse joint attention network under variable working conditions

Cited by 2 publications

References 38 publications

Open-set domain adaptive fault diagnosis based on supervised contrastive learning and a complementary weighted dual adversarial network

Open-set domain adaptive fault diagnosis based on supervised contrastive learning and a complementary weighted dual adversarial network

MDSC-FSPPA-LCFF network for diagnosis of rolling bearing with multipoint fault

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