研究论文
Digital twin-assisted self-supervised contrastive learning: A novel framework for electromechanical equipment fault diagnosis
Jiawei Lu, Chao Lü, Qibing Wang, Yuchen He, Binchun Xia, Xudong Zhang
China Jiliang University Zhejiang Shuren University
来源ISA Transactions
年份2025
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逐年被引趋势
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226
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2
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0.76
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同类平均 = 1
同类平均 = 1
前 28%
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学术脉络
学科主题
工程Machine Fault Diagnosis Techniques
Machine Learning and ELM · Structural Health Monitoring Techniques
参考文献 60
Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark study
被引 2,720Wade A. Smith, Robert Bond Randall · Mechanical Systems and Signal Processing · 2015
A New Deep Learning Model for Fault Diagnosis with Good Anti-Noise and Domain Adaptation Ability on Raw Vibration Signals
被引 1,756Wěi Zhāng, Gaoliang Peng, Chuanhao Li · Sensors · 2017
A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load
被引 1,416Wěi Zhāng, Chuanhao Li, Gaoliang Peng · Mechanical Systems and Signal Processing · 2017
此处列出前 3 条
引用本文 2
Genetic algorithm–optimized loss balancing in physics-informed neural networks for manufacturing digital twin applications
被引 1Aswin Karkadakattil · Journal of Intelligent Manufacturing and Special Equipment · 2026
Fault Diagnosis of Electromechanical Equipment based on Adaptive Correlation-based Multi-Sensor Convolutional Neural Network
被引 0Yueqiao Zhang · 2026
按被引量排序，此处列出前 3 条