学术专著

  1. Yaguo Lei, Intelligent Fault Diagnosis and Remaining Useful Life Prediction of Rotating Machinery [M]. Elsevier Butterworth-Heinemann, Oxford, 2016.（参与撰写第1、6两章内容，获第十七届输出版优秀图书奖）
  2. Yaguo Lei, Naipeng Li, Xiang Li, Big Data-Driven Intelligent Fault Diagnosis and Prognosis for Mechanical Systems [M]. Springer, 2022.（获国家科学技术出版基金资助，获第二十三届输出版优秀图书奖）

            

 

发表学术期刊论文

2024年

  1. Naipeng Li, Mingyang Wang, Yaguo Lei*, Xiaosheng Si, Bin Yang, Xiang Li, A nonparametric degradation modeling method for remaining useful life prediction with fragment data, Reliability Engineering & System Safety, 2024, 249: 110224.（中科院一区Top）
  2. Xiao Cai, Naipeng Li*, Min Xie. RUL prediction for two-phase degrading systems considering physical damage observations, Reliability Engineering & System Safety, 2024, 244: 109926.（中科院一区Top）
  3. Yuan Wang, Lei, Yaguo Lei*, Naipeng Li, Xiang Li, Bin Yang, Multimodal correlation-aware fusion framework for enhanced machinery health prognosis with unlabeled and low-quality data exploitation, IEEE Transactions on Neural Networks and Learning Systems, 2024. (Early Access, 中科院一区Top)
  4. Xiaofei Liu, Naipeng Li, Yaguo Lei*, Dong Wang, Qubing Ren, Jinze Jiang, Yuan Wang, Optimal weight impulse extraction: New impulse extraction methodology for incipient gearbox condition monitoring, Mechanical Systems and Signal Processing, 2024, 216: 111449.（中科院一区Top）
  5. Yuan Wang, Yaguo Lei*, Naipeng Li, Xuanyu Gao, Xiaofei Liu, Qubing Ren, Jinze Jiang, A multimodal dynamic parameterized bilinear factorized framework for remaining useful life prediction under variational data, Reliability Engineering & System Safety, 2024, 245: 110025.（中科院一区Top）

2023年

  1. Naipeng Li, Yaguo Lei*, Xiaofei Liu, Tao Yan, Pengcheng Xu, Machinery health prognostics with multimodel fusion degradation modeling, IEEE Transactions on Industrial Electronics, 2023, 70(11): 11764-11773. (中科院一区Top)
  2. Naipeng Li, Yaguo Lei, Xiang Li*, Xiaofei Liu, Bin Yang, A new nonparametric degradation modeling method for truncated degradation signals by axis rotation, Mechanical Systems and Signal Processing, 2023, 192: 110213.（中科院1区Top）
  3. Xiang Li, Yixiao Xu, Naipeng Li*, Bin Yang, Yaguo Lei. Remaining useful life prediction with partial sensor malfunctions using deep adversarial networks. IEEE/CAA Journal of Automatica Sinica, 2023, 10(1): 121-134.（ESI高被引，中科院1区Top）
  4. Yuan Wang, Yaguo Lei*, Naipeng Li, Tao Yan, Xiaosheng Si. Deep multisource parallel bilinear-fusion network for remaining useful life prediction of machinery. Reliability Engineering & System Safety, 2023, 231: 109006. （中科院一区Top）
  5. Tao Yan, Yaguo Lei*, Naipeng Li, Liliane Pintelon, Reginald Dewil. Joint optimization of maintenance and spare parts inventory for multi-unit systems with a generalized structure. Journal of Manufacturing Science and Engineering-Transactions of the ASME, 2023, 145(4): 041001.
  6. Xiaofei Liu, Yaguo Lei*, Naipeng Li, Xiaosheng Si, Xiang Li. RUL prediction of machinery using convolutional-vector fusion network through multi-feature dynamic weighting. Mechanical Systems and Signal Processing, 2023, 185: 109788.（中科院1区Top）

2022年

  1. Naipeng Li, Pengcheng Xu, Yaguo Lei*, Xiao Cai, Detong Kong, A self-data-driven method for remaining useful life prediction of wind turbines considering continuously varying speeds. Mechanical Systems and Signal Processing, 2022, 165: 108315.（中科院1区Top）
  2. Tao Yan, Yaguo Lei*, Naipeng Li, Xiaosheng Si, Liliane Pintelon, Reginald Dewil. Online joint replacement-order optimization driven by a nonlinear ensemble remaining useful life prediction method. Mechanical Systems and Signal Processing, 2022, 173:109053.（中科院1区Top）
  3. Zhijian Wang*, Xinxin He, Bin Yang, Naipeng Li. Subdomain adaptation transfer learning network for fault diagnosis of roller bearings. IEEE Transactions on Industrial Electronics, 2022, 69(8): 8430-8439.（ESI高被引，中科院1区Top）
  4. 雷亚国*, 杨彬, 李乃鹏, 李响, 武通海. 跨设备的机械故障靶向迁移诊断方法. 机械工程学报, 2022, 58(12): 1-9.
  5. 韩特, 李彦夫*, 雷亚国, 李乃鹏, 李响. 融合图标签传播和判别特征增强的工业机器人关键部件半监督故障诊断方法. 机械工程学报, 2022, 58(17): 116-124.
  6. 贾峰, 李世豪, 沈建军*, 马军星, 李乃鹏. 采用深度迁移学习与自适应加权的滚动轴承故障诊断. 西安交通大学学报, 2022, 56(08): 1-10.

2021年

  1. Naipeng Li, Yaguo Lei*, Nagi Gebraeel, Zhijian Wang, Xiao Cai, Pengcheng Xu, Biao Wang. Multi-sensor data-driven remaining useful life prediction of semi-observable systems. IEEE Transactions on Industrial Electronics, 2021, 68(11): 11482-91.（中科院1区Top）
  2. Naipeng Li, Nagi Gebraeel, Yaguo Lei*, Xiaolei Fang, Xiao Cai, Tao Yan. Remaining useful life prediction based on a multi-sensor data fusion model. Reliability Engineering & System Safety, 2021, 208: 107249.（中科院1区Top）
  3. Tao Yan, Yaguo Lei*, Naipeng Li, Biao Wang, Wenting Wang. Degradation modeling and remaining useful life prediction for dependent competing failure processes. Reliability Engineering & System Safety, 2021, 212: 107638. （中科院一区Top）
  4. Biao Wang, Yaguo Lei*, Naipeng Li, Wenting Wang. Multiscale convolutional attention network for predicting remaining useful life of machinery. IEEE Transactions on Industrial Electronics, 2021, 68(8): 7496-7504.（ESI热点，中科院1区Top）
  5. Zhijian Wang*, Chen Wang, Naipeng Li. Bearing fault diagnosis method based on similarity measure and ensemble learning. Measurement Science and Technology, 2021, 32(5): 055005.
  6. Zhijian Wang*, Ningning Yang, Naipeng Li, Wenhua Du, Junyuan Wang. A new fault diagnosis method based on adaptive spectrum mode extraction. Structural Health Monitoring-An International Journal, 2021, 20(6): 3354-3370.（ESI高被引，ESI高被引）
  7. Bin Yang, Chi-Guhn Lee, Yaguo Lei*, Naipeng Li, Na Lu. Deep partial transfer learning network: A method to selectively transfer diagnostic knowledge across related machines. Mechanical Systems and Signal Processing, 2021, 156: 107618.（中科院1区Top）
  8. Zhijian Wang*, Wenlei Zhao, Wenhua Du, Naipeng Li, Junyuan Wang. Data-driven fault diagnosis method based on the conversion of erosion operation signals into images and convolutional neural network. Process Safety and Environmental Protection, 2021, 149: 591-601.
  9. Saibo Xing, Yaguo Lei*, Shuhui Wang, Na Lu, Naipeng Li. A label description space embedded model for zero-shot intelligent diagnosis of mechanical compound faults. Mechanical Systems and Signal Processing, 2021, 162: 108036.（中科院1区Top）
  10. 李乃鹏, 蔡潇, 雷亚国*, 徐鹏程, 王彪. 一种融合多传感器数据的数模联动机械剩余寿命预测方法. 机械工程学报, 2021, 57(20): 29-46.
  11. 雷亚国*, 许学方, 蔡潇, 李乃鹏, 孔德同, 张勇铭. 面向机械装备健康监测的数据质量保障方法研究. 机械工程学报, 2021, 57(4): 1-9.

2020年

  1. Biao Wang, Yaguo Lei*, Naipeng Li, Ningbo Li. A hybrid prognostics approach for estimating remaining useful life of rolling element bearings. IEEE Transactions on Reliability, 2020, 69(1): 401-412.（ESI热点）
  2. Bin Yang, Yaguo Lei*, Feng Jia, Naipeng Li, Zhaojun Du. A polynomial kernel induced distance metric to improve deep transfer learning for fault diagnosis of machines. IEEE Transactions on Industrial Electronics, 2020, 67(11): 9747-9757.（ESI高被引，中科院1区Top）
  3. Biao Wang, Yaguo Lei*, Tao Yan, Naipeng Li, Liang Guo. Recurrent convolutional neural network: A new framework for remaining useful life prediction of machinery. Neurocomputing, 2020, 379: 117-129. （ESI高被引）
  4. Yaguo Lei*, Bin Yang, Xinwei Jiang, Feng Jia, Naipeng Li, Asoke K. Nandi. Applications of machine learning to machine fault diagnosis: A review and roadmap. Mechanical Systems and Signal Processing, 2020, 138: 106587.（ESI热点，中科院1区Top）
  5. Tao Yan, Yaguo Lei*, Biao Wang, Tianyu Han, Xiaosheng Si, Naipeng Li. Joint maintenance and spare parts inventory optimization for multi-unit systems considering imperfect maintenance actions. Reliability Engineering and System Safety, 2020, 202: 106994.（中科院1区Top）

2020年以前

  1. Naipeng Li, Nagi Gebraeel, Yaguo Lei*, Linkan Bian, Xiaosheng Si. Remaining useful life prediction of machinery under time-varying operating conditions based on a two-factor state-space model. Reliability Engineering & System Safety, 2019, 186: 88-100.（中科院1区Top）
  2. Naipeng Li, Yaguo Lei*, Tao Yan, Ningbo Li, Tianyu Han. A Wiener-process-model-based method for remaining useful life prediction considering unit-to-unit variability. IEEE Transactions on Industrial Electronics, 2019, 66(3): 2092-2101.（ESI高被引，中科院1区Top）
  3. Naipeng Li, Yaguo Lei*, Liang Guo, Tao Yan, Jing Lin. Remaining useful life prediction based on a general expression of stochastic process models. IEEE Transactions on Industrial Electronics, 2017, 64(7): 5709-5718.（中科院1区Top）
  4. Naipeng Li, Yaguo Lei*, Jing Lin, Steven X. An improved exponential model for predicting remaining useful life of rolling element bearings. IEEE Transactions on Industrial Electronics, 2015, 62(12): 7762-7773.（ESI高被引，中科院1区Top）
  5. Yaguo Lei*, Naipeng Li, Liang Guo, Ningbo Li, Tao Yan, Jing Lin. Machinery health prognostics: A systematic review from data acquisition to RUL prediction. Mechanical Systems and Signal Processing, 2018, 104: 799-834. （ESI热点，中科院1区Top）
  6. Liang Guo, Naipeng Li, Feng Jia, Yaguo Lei, Jing Lin. A recurrent neural network based health indicator for remaining useful life prediction of bearings. Neurocomputing, 2017, 240: 98-109.（ESI高被引）
  7. Yaguo Lei*, Naipeng Li, Szymon Gontarz, Jing Lin, Stanislaw Radkowski, Jacek Dybala. A model-based method for remaining useful life prediction of machinery. IEEE Transactions on Reliability, 2016, 65(3): 1314-1326.（ESI高被引）
  8. Yaguo Lei*, Naipeng Li, Jing Lin. A new method based on stochastic process models for machine remaining useful life prediction. IEEE Transactions on Instrumentation and Measurement, 2016, 65(12): 2671-2684.
  9. Yaguo Lei*, Naipeng Li, Jing Lin, Zhengjia He. Two new features for condition monitoring and fault diagnosis of planetary gearboxes. Journal of Vibration and Control, 2015, 21(4): 755-764.
  10. Lang Xue, Naipeng Li, Yaguo Lei*, Ningbo Li. Incipient fault detection for rolling element bearings under varying speed conditions. Materials, 2017, 10(6): 675.
  11. Biao Wang, Yaguo Lei*, Naipeng Li, Tao Yan. Deep separable convolutional network for remaining useful life prediction of machinery. Mechanical Systems and Signal Processing, 2019, 134: 106330.（ESI高被引, 中科院1区Top）
  12. Zijian Qiao, Yaguo Lei*, Naipeng Li. Applications of stochastic resonance to machinery fault detection: A review and tutorial. Mechanical Systems and Signal Processing, 2019, 122: 502-536.（ESI高被引, 中科院1区Top）
  13. Liang Guo, Yaguo Lei*, Naipeng Li, Tao Yan, Ningbo Li. Machinery health indicator construction based on convolutional neural networks considering trend burr. Neurocomputing, 2018, 292: 142-150.
  14. Yaguo Lei*, Zongyao Liu, Xionghui Wu, Naipeng Li, Wu Chen, Jing Lin. Health condition identification of multi-stage planetary gearboxes using a mRVM-based method. Mechanical Systems and Signal Processing, 2015, 60-61: 289-300.（中科院1区Top）
  15. Liang Guo, Yaguo Lei*, Saibo Xing, Tao Yan, Naipeng Li. Deep convolutional transfer learning network: A new method for intelligent fault diagnosis of machines with unlabeled data. IEEE Transactions on Industrial Electronics, 2019, 66(9): 7316-7325.（ESI热点，IEEE工业电子协会杰出论文奖，中科院1区Top）
  16. 雷亚国*, 陈吴, 李乃鹏, 林京. 自适应多核组合相关向量机预测方法及其在机械设备剩余寿命预测中的应用. 机械工程学报, 2016, 52(1): 87-93.
  17. 雷亚国*, 孔德同, 李乃鹏, 林京. 自适应总体平均经验模式分解及其在行星齿轮箱故障检测中的应用. 机械工程学报, 2014, 50(3): 64-70.
  18. 雷亚国*, 韩天宇, 王彪, 李乃鹏, 闫涛, 杨军. XJTU-SY滚动轴承加速寿命试验数据集解读. 机械工程学报, 2019, 55(16): 1-6.

国家标准

  1. GB/T 42983.1-2023，工业机器人运行维护 第1部分：在线监测，实施日期2024-04-01.
  2. GB/T 42983.2-2023，工业机器人运行维护 第2部分：故障诊断，实施日期2024-04-01.
  3. GB/T 42983.3-2023，工业机器人运行维护 第3部分：健康评估，实施日期2024-04-01.
  4. GB/T 42983.4-2023，工业机器人运行维护 第4部分：预测性维护，实施日期2024-04-01.

授权国家发明专利

  1. 雷亚国; 李乃鹏; 陈吴; 林京. 基于特征融合和粒子滤波的滚动轴承剩余寿命预测方法, 申请号：2014101359952, 授权公告日：2017.02.15.（已转让）
  2. 雷亚国; 薛朗; 李乃鹏; 周昕; 林京. 一种基于报警次数跳变触发机制的机械早期故障判别方法, 申请号: 2016109570832, 授权公告日: 2018.08.07.（已转让）
  3. 李乃鹏; 蔡潇; 雷亚国; 韩天宇; 王彪. 一种敏感测点选择及融合的机床铣刀剩余寿命预测方法, 申请号: 2019113536166, 授权公告日: 2021.11.16.
  4. 雷亚国; 李乃鹏; 林京; 薛朗. 一种模拟随机粒子衰退轨迹的剩余寿命求解方法, 申请号: 2016106050643, 授权公告日: 2018.11.09.
  5. 雷亚国; 李乃鹏; 林京; 廖与禾; 周昕. 滚动轴承集成期望最大化和粒子滤波的寿命预测方法, 申请号: 2015100333979, 授权公告日: 2017.05.17.
  6. 雷亚国; 蔡潇; 李乃鹏; 徐鹏程; 刘晓飞; 赵军. 一种风力发电机基准工况转换方法, 申请号: 2021103035981, 授权公告日: 2022.01.11.
  7. 雷亚国; 周昕; 李乃鹏; 单洪凯; 林京. 一种基于自相关零点计数的风机轴承阶变信号识别方法, 申请号: 2016110011548, 授权公告日: 2019.03.26.
  8. 杨彬; 雷亚国; 李乃鹏; 司小胜. 域不对称因子加权的滚动轴承故障深度局部迁移诊断方法, 申请号: 2020102269342, 授权公告日：2020.12.29.
  9. 雷亚国; 李宁波; 李乃鹏; 闫涛; 林京. 一种梯形噪声分布的指数模型机械设备剩余寿命预测方法, 申请号: 2017101098763, 授权公告日: 2020.02.18.
  10. 雷亚国; 王文廷; 李乃鹏; 邢赛博; 王彪; 杨彬; 李熹伟; 姜鑫伟. 基于状态划分与频段同步校正的三相电机故障诊断方法, 申请号: 2021104503809, 授权公告日: 2022.03.22
  11. 雷亚国; 韩天宇; 李乃鹏; 王彪. 一种多工况动态基准化的机械设备剩余寿命预测方法, 申请号: 2018109225865, 授权公告日: 2020.04.10.
  12. 雷亚国; 王远; 杨彬; 李乃鹏. 多项式核植入特征分布适配的滚动轴承故障迁移诊断方法, 申请号: 2019106195063, 授权公告日: 2020.05.26.
  13. 雷亚国; 林京; 周昕; 李乃鹏. 一种双重更新的四因素随机退化模型齿轮寿命预测方法, 申请号: 2016102571020, 授权公告日: 2019.03.12.
  14. 雷亚国; 林京; 陈吴; 李乃鹏. 一种自适应多核组合相关向量机的滚动轴承寿命预测模型, 申请号：2015100354886, 授权公告日: 2017.06.20.
  15. 雷亚国; 贾峰; 周昕; 李乃鹏; 林京. 一种基于堆叠自动编码机的行星齿轮箱智能诊断方法, 申请号: 2015101588265, 授权公告日: 2017.02.22.
  16. 雷亚国; 杜兆钧; 杨彬; 李乃鹏. 一种基于适配共享深度残差网络的机械故障迁移诊断方法. 申请号: 2018109201589, 授权公告日: 2021.05.04.
  17. 雷亚国; 姜鑫伟; 王彪; 李乃鹏. 一种基于层叠分离卷积模块的机械设备剩余寿命预测方法, 申请号: 2019102356920, 授权公告日: 2020.07.28.
  18. 雷亚国; 何平; 邢赛博; 李乃鹏; 武通海. 基于自搜索特征峰值与局部极差的离心泵故障诊断方法, 申请号: 2021107596839, 授权公告日: 2022.04.05.
  19. 雷亚国; 吴雄辉; 林京; 李乃鹏. 基于多分类相关向量机的行星齿轮箱太阳轮故障分类方法, 申请号：2013102727302, 授权公告日：2016.03.02.
  20. 雷亚国; 牛善涛; 郭亮; 李乃鹏; 林京. 基于循环神经网络融合的机械零部件健康指标构造方法, 申请号: 2017101098778, 授权公告日: 2020.03.31.
  21. 雷亚国; 姜鑫伟; 邢赛博; 李乃鹏; 司小胜. 一种基于期望差异约束置信网络的机械设备故障诊断方法, 申请号: 201910466135X, 授权公告日: 2020.03.31.
  22. 雷亚国; 韩天宇; 牛善涛; 李乃鹏; 邢赛博; 闫涛. 基于距离度量学习的机械关键部件虚拟退化指标构造方法, 申请号: 2018105481716, 授权公告日: 2019.11.26.
  23.   雷亚国; 王文廷; 邢赛博; 李乃鹏; 杨彬; 王彪; 姜鑫伟; 李熹伟. 一种转频谱峰与电流极差联合推断轴流风机故障诊断方法, 申请号: 2021104503847, 授权公告日: 2022.10.28.
  24. 李响; 何平; 雷亚国; 杨彬; 李乃鹏; 曹军义; 武通海. 一种轻量化通信的滚动轴承多用户协同智能故障诊断方法, 申请号: 2021115515443, 授权公告日: 2022.10.28.
  25. 雷亚国; 李则达; 许学方; 周昕; 李乃鹏. 一种基于核估计LOF的机械监测数据异常段检测方法, 申请号: 2018111448325, 授权公告日: 2020.05.15.
  26. 雷亚国; 李则达; 许学方; 周昕; 李乃鹏. 一种基于SES-LOF的旋转机械监测数据噪点检测方法, 申请号: 201811144833X, 授权公告日: 2020.05.15.
  27. 雷亚国; 王文彬; 邢赛博; 杨彬; 李熹伟; 李乃鹏. 曹军义一种故障语义空间内嵌的滚动轴承复合故障诊断方法, 申请号: 2021108110264, 授权公告日: 2022.06.07.