研究论文
A lightweight residual network based on improved knowledge transfer and quantized distillation for cross-domain fault diagnosis of rolling bearings
Wei Guo, Xiang Li, Ziqian Shen
University of Electronic Science and Technology of China
来源Expert Systems with Applications
年份2023
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学术脉络
学科主题
工程Machine Fault Diagnosis Techniques
Gear and Bearing Dynamics Analysis · Occupational Health and Safety Research
参考文献 55
Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark study
被引 2,697Wade A. Smith, Robert B. Randall · Mechanical Systems and Signal Processing · 2015
Bayesian Dark Knowledge
被引 134Anoop Korattikara, Vivek Rathod, Kevin J. Murphy · arXiv (Cornell University) · 2015
Domain-Adversarial Training of Neural Networks
被引 7,716Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan · Advances in computer vision and pattern recognition · 2017
此处列出前 3 条
引用本文 45
MRCFN: A multi-sensor residual convolutional fusion network for intelligent fault diagnosis of bearings in noisy and small sample scenarios
被引 140Maoyou Ye, Xiaoan Yan, Xing Hua · Expert Systems with Applications · 2024
Review of intelligent fault diagnosis for rotating machinery under imperfect data conditions
被引 39Hao Chen, Jiaming Li, Xianbo Wang · Expert Systems with Applications · 2025
Deep discriminative domain adaptation network considering sampling frequency for cross-domain mechanical fault diagnosis
被引 27Guiping Chen, Dong Xiang, Tingting Liu · Expert Systems with Applications · 2025
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