Explicit speed-integrated LSTM network for non-stationary gearbox vibration representation and fault detection under varying speed conditions

Chen, Yuejian; Liu, Xuemei; Rao, Meng; Qin, Yong; Wang, Zhipeng; Ji, Yuanjin

doi:10.1016/j.ress.2024.110596

Reliability Engineering & System Safety

2025

DOI: 10.1016/j.ress.2024.110596

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Explicit speed-integrated LSTM network for non-stationary gearbox vibration representation and fault detection under varying speed conditions

Yuejian Chen,

Xuemei Liu,

Meng Rao

et al.

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Cited by 3 publications

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Advanced VTSDREF for vehicle-turnout system dynamic reliability analysis: Integration of hybrid deep learning and adaptive probability density evolution method

Tang,

Cai,

Wang

et al. 2025

Reliability Engineering & System Safety

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Advanced VTSDREF for vehicle-turnout system dynamic reliability analysis: Integration of hybrid deep learning and adaptive probability density evolution method

Tang,

Cai,

Wang

et al. 2025

Reliability Engineering & System Safety

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MSTD: a framework for rolling bearing fault diagnosis based on multi-scale and soft-threshold denoising

Qiao,

Yao,

Yang

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Nondestructive Testing and Evaluation

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Representative Community Detection Algorithms for Attribute Networks

Chen,

Xie,

et al. 2024

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In network analysis, methods for identifying a community structure of ten involve optimizing a specific objective function to achieve a single optimal allocation from network nodes to communities. In practice, however, we often encounter multiple division schemes with high-quality scores close to the overall optimum. An accurate depiction of the community structure is more appropriately achieved by a series of high-quality division schemes rather than relying solely on a single optimal solution. However, such a collection of network divisions may be challenging to interpret, as its size may rapidly expand to hundreds or even thousands. To this end, we propose a representative community detection algorithm for attribute networks. By clustering similar network partitions and selecting representative partitions from each cluster, we can comprehensively reveal the diversity of network community structures and provide partition results with a more global perspective. Network partitioning experiments on natural and artificial datasets demonstrate that our proposed method performs better than advanced methods.

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Explicit speed-integrated LSTM network for non-stationary gearbox vibration representation and fault detection under varying speed conditions

Cited by 3 publications

References 34 publications

Advanced VTSDREF for vehicle-turnout system dynamic reliability analysis: Integration of hybrid deep learning and adaptive probability density evolution method

Advanced VTSDREF for vehicle-turnout system dynamic reliability analysis: Integration of hybrid deep learning and adaptive probability density evolution method

MSTD: a framework for rolling bearing fault diagnosis based on multi-scale and soft-threshold denoising

Representative Community Detection Algorithms for Attribute Networks

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