[1]雷亚国,贾峰,孔德同,等.大数据下机械智能故障诊断的机遇与挑战[J].机械工程学报, 2018, 54(5):94-104.
LEI Yaguo, JIA Feng, KONG Detong, et al. Opportunities and Challenges of Mechanical Intelligent Fault Diagnosis under Big Data [J]. Journal of Mechanical Engineering, 2018, 54(5):94-104.
[2]陈雪峰, 郭艳婕, 许才彬,等. 风电装备故障诊断与健康监测研究综述[J]. 中国机械工程,2021,31(2):175-189.
CHEN Xuefeng, GUO Yanjie, XU Caibin, et al. Review of Fault Diagnosis and Health Monitoring for Wind Power Equipment [J]. China Mechanical Engineering, 2021,31(2):175-189.
[3]LEI Yaguo, YANG Bin, JIANG Xinwei, et al. Applications of Machine Learning to Machine Fault Diagnosis:a Review and Roadmap [J]. Mechanical Systems and Signal Processing, 2020, 138:106587.
[4]文成林, 吕菲亚. 基于深度学习的故障诊断方法综述[J]. 电子与信息学报, 2020, 42(1):234-248.
WEN Chenglin, LYU Feiya. Review on Deep Learning Based Fault Diagnosis [J]. Journal of Electronics & Information Technology, 2020, 42(1):234-248.
[5]SHAO H, JIANG H, ZHAO H, et al. A Novel Deep Autoencoder Feature Learning Method for Rotating Machinery Fault Diagnosis [J]. Mechanical Systems and Signal Processing, 2017, 95:187-204.
[6]JIA Feng, LEI Yaguo, GUO Liang, et al. A Neural Network Constructed by Deep Learning Technique and Its Application to Intelligent Fault Diagnosis of Machines [J]. Neurocomputing, 2018, 272:619-628.
[7]SUN Jiedi, YAN Changhong, WEN Jiangtao. Intelligent Bearing Fault Diagnosis Method Combining Compressed Data Acquisition and Deep Learning [J]. IEEE Transactions on Instrumentation and Measurement, 2018, 67(1):185-195.
[8]JIANG Guoqian, HE Haibo, XIE Ping, et al. Stacked Multilevel-Denoising Autoencoders:a New Representation Learning Approach for Wind Turbine Gearbox Fault Diagnosis [J]. IEEE Transactions on Instrumentation and Measurement, 2017, 66 (9):2391-2402.
[9]JIANG Guoqian, HE Haibo, YAN Jun, et al. Multiscale Convolutional Neural Networks for Fault Diagnosis of Wind Turbine Gearbox [J]. IEEE Transactions on Industrial Electronics, 2019, 66(4):3196-3207.
[10]周奇才, 刘星辰, 赵炯, 等. 旋转机械一维深度卷积神经网络故障诊断研究[J]. 振动与冲击,2018, 37(23):31-37.
ZHOU Qicai, LIU Xingchen, ZHAO Jiong. Fault Diagnosis for Rotating Machinery Based on 1D Depth Convolutional Neural Network [J]. Journal of Vibration and Shock, 2018, 37(23):31-37.
[11]曲建岭,余路,袁涛,等.基于一维卷积神经网络的滚动轴承自适应故障诊断算法[J].仪器仪表学报,2018, 39(7):134-143.
QU Jianling, YU Lu, YUAN Tao, et al. Adaptive Fault Diagnosis Algorithm for Rolling Bearing Based on One-Dimensional Convolutional Neural Network [J]. Chinese Journal of Scientific Instrument, 2018, 39(7):134-143.
[12]WEN Long, LI Xinyu, GAO Liang, et al. A New Convolutional Neural Network-based Data-Driven Fault Diagnosis Method [J]. IEEE Transactions on Industrial Electronics, 2018, 65(7):5990-5998.
[13]WANG Jianyu, MO Zhenlin, ZHANG Heng, et al. A Deep Learning Method for Bearing Fault Diagnosis Based on Time-Frequency Image [J]. IEEE Access, 2019, 7:42373-42383.
[14]GUO Liang, LEI Yaguo, LI Naipeng, et al. Machinery Health Indicator Construction Based on Convolutional Neural Networks Considering Trend Burr [J]. Neurocomputing, 2018, 292:142-150.
[15]PAN S J, YANG Q. A Survey on Transfer Learning [J]. IEEE Transactions on Knowledge and Data Engineering, 2010, 22(10):1345-1359.
[16]LI X, ZHANG W, DING Q. Cross-domain Fault Diagnosis of Rolling Element Bearings Using Deep Generative Neural Networks [J]. IEEE Transactions on Industrial Electronics, 2019, 66(7):5525-5534.
[17]SHAO S Y, MCALEER S, YAN RQ, et al. Highly Accurate Machine Fault Diagnosis Using Deep Transfer Learning [J]. IEEE Transactions onIndustrial Informatics, 2019, 15(4):2446-2455.
[18]HE K , ZHANG X , REN S , et al. Deep Residual Learning for Image Recognition[C]∥ IEEE Conference on Computer Visionand Pattern Recognition. IEEE Computer Society, 2016:770-778.
[19]LIU Chongdang, ZHANG Linxuan, YAO Rong, et al. Dual Attention-Based Temporal Convolutional Network for Fault Prognosis under Time-varying Operating Conditions [J]. IEEE Transactions on Instrumentation and Measurement, 2021, 99:1-10.
[20]CHE Changchang, WANG Huawei, NI Xiaomei, et al. Domain Adaptive Deep Belief Network for Rolling Bearing Fault Diagnosis [J]. Computer & Industrial Engineering, 2020, 143:106427.
[21]WU Y, HE K. Group Normalization [J]. International Journal of Computer Vision, 2018, 128(3):742-755.
[22]SMITH W A, RANDALL R B. Rolling Element Bearing Diagnostics Using the Case Western Reserve University Data:a Benchmark Study [J]. Mechanical Systems and Signal Processing, 2015, 64:100-131.
[23]LI Y, WANG X, SI S, et al. Entropy Based Fault Classification Using the Case Western Reserve University Data:a Benchmark Study [J]. IEEE Transactions on Reliability, 2019, 69(2):754-767.
[24]LI X, ZHANG W, XU N X, et al. Deep Learning-Based Machinery Fault Diagnostics with Domain Adaptation Across Sensors at Different Places [J]. IEEE Transactions on Industrial Electronics, 2019, 67(8):6785-6794.
[25]WANG Huaqing, LI Shi, SONG Liuyang, et al. An Enhanced Intelligent Diagnosis Method Based on Multi-sensor Image Fusion via Improved Deep Learning Network[J]. IEEE Transactions on Instrumentation and Measurement, 2020, 69(6):2648-2657.
[26]郭亮,董勋,高宏力,等. 无标签数据下基于特征知识迁移的机械设备智能故障诊断[J].仪器仪表学报, 2019, 40(8):61-67.
GUO Liang, DONG Xun, GAO Hongli, et al. Intelligent Fault Diagnosis of Mechanical Equipment Based on Feature Knowledge Transfer under Unlabeled Data [J]. Chinese Journal of Scientific Instrument, 2019, 40(8):61-67.
[27]YANG B , LEI Y , JIA F , et al. An Intelligent Fault Diagnosis Approach Based on Transfer Learning from Laboratory Bearings to Locomotive Bearings [J]. Mechanical Systems and Signal Processing, 2019, 122:692-706.
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