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	Unsupervised domain adaptation of bearing fault diagnosis based on Join Sliced Wasserstein Distance 期刊论文 ISA Transactions, 2022 作者: Chen, Pengfei; Zhao, Rongzhen; He, Tianjing; Wei, Kongyuan; Yang, Qidong 收藏 \| 浏览/下载：9/0 \| 提交时间：2022/07/20 Bearings (machine parts) Deep neural networks Fault detection Large dataset Probability distributions Bearing fault diagnosis Conditional probabilities Domain adaptation Faults diagnosis Mechanical faults diagnosis Mechanical systems Noise interference Pseudo label Sliced wasserstein distances Unsupervised domain adaptation
	Multiresolution Hypergraph Neural Network for Intelligent Fault Diagnosis 期刊论文 IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2022, 卷号: 71, 页码: 10 作者: Yan,Xunshi; Liu Y(刘洋); Zhang CA(张陈安) 收藏 \| 浏览/下载：7/0 \| 提交时间：2022/11/14 Convolution Fault diagnosis Correlation Task analysis Machinery Machine learning algorithms Laplace equations Fault diagnosis graph convolutional network (GCN) hypergraph hypergraph neural network (HGNN) multiresolution
	Multi-feature fusion for fault diagnosis of rotating machinery based on convolutional neural network 期刊论文 COMPUTER COMMUNICATIONS, 2021, 卷号: 173 作者: Liu, Shaoqing; Ji, Zhenshan; Wang, Yong; Zhang, Zuchao; Xu, Zhanghou 收藏 \| 浏览/下载：43/0 \| 提交时间：2021/06/15 Fault diagnosis Feature fusion Multi-feature Convolutional Neural Network (CNN) Light Gradient Boosting Machine (LightGBM)
	Evolving Container to Unikernel for Edge Computing and Applications in Process Industry 期刊论文 PROCESSES, 2021, 卷号: 9, 期号: 2, 页码: 18 作者: Chen, Shichao; Zhou, Mengchu 收藏 \| 浏览/下载：52/0 \| 提交时间：2021/03/29 big data analytics lightweight virtualization cloud computing edge computing industrial process Industry 4.0 Internet of things machine learning process industry fault diagnosis
	Research on Fault Diagnosis Method of Rod Pumping Wells Based on CNN_APRCSO_SVM 会议论文 Chicago, May 26-28, 2021 作者: Wang MX(王明新); Zang CZ(臧传治); Ji ZP(纪振平); He YP(贺云鹏); Zeng P(曾鹏) 收藏 \| 浏览/下载：12/0 \| 提交时间：2021/08/21 Rod pumping well Fault diagnosis Indicator diagrams Convolutional neural network (CNN) Support vector machine (SVM) Chicken swarm optimization (CSO) Algorithm optimization
	A novel classification method based on ICGOA-KELM for fault diagnosis of rolling bearing 期刊论文 Applied Intelligence, 2020, 卷号: 50, 期号: 无, 页码: 2833-2847 作者: Peng Chen; Xiaoqiang Zhao; Qixian Zhu 收藏 \| 浏览/下载：14/0 \| 提交时间：2020/11/06 Chaotic systems Computer aided diagnosis Damage detection Failure analysis Fault detection Machine learning Optimization Extreme learning machine Fault detection and classification Global searching ability Heuristic optimization algorithms ICGOA- KELM Intrinsic Mode functions Optimization algorithms Rolling bearings
	A new approach for fault diagnosis with full-scope simulator based on state information imaging in nuclear power plant 期刊论文 ANNALS OF NUCLEAR ENERGY, 2020, 卷号: 141 作者: Yao, Yuantao; Wang, Jin; Xie, Min; Hu, Liqin; Wang, Jianye 收藏 \| 浏览/下载：26/0 \| 提交时间：2020/11/26 NPP Fault diagnosis State information imaging Machine learning Kernel principal component analysis F1 score
	A Survey on Fault Diagnosis and Fault Tolerant Methodologies for Permanent Magnet Synchronous Machines 期刊论文 International Journal of Automation and Computing, 2020, 卷号: 17, 期号: 6, 页码: 763-787 作者: Erphan A. Bhuiyan; Md. Maeenul Azad Akhand; Sajal K. Das; Md. F. Ali; Z. Tasneem 收藏 \| 浏览/下载：13/0 \| 提交时间：2021/02/22 Permanent magnet synchronous machine (PMSM) fault diagnosis fault tolerant control system (FTCS) fault detection stability.
	DPCA与GA-SVM融合的智能台车液压系统故障诊断期刊论文控制工程, 2020, 卷号: 27, 期号: 11, 页码: 1980 作者: 陈昭明; 徐泽宇; 赵迎收藏 \| 浏览/下载：11/0 \| 提交时间：2021/03/04 Hydraulic system fault diagnosis dynamic principal component analysis genetic algorithm support vector machine 液压系统故障诊断动态主成分分析遗传算法支持向量机
	A New Fault Diagnosis Method for Rotating Machinery Based on SCA-FastICA 期刊论文 Mathematical Problems in Engineering, 2020, 卷号: 2020 作者: Miao, Feng; Zhao, Rongzhen 收藏 \| 浏览/下载：3/0 \| 提交时间：2020/11/14 Blind source separation Failure analysis Independent component analysis Rotating machinery Signal denoising Vibration analysis Conventional methods De-noised signals Fast independent component analysis Fault diagnosis method Machine health monitoring Noise and vibration Simulation tests Vibration sources

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