姓名： 余建波
性别： 男
出生年月：

职称：
教授
党政职务：

研究方向：
智能制造，设备智能维护, 先进质量控制，人工智能
导师类型： 博导
通讯地址：
同济大学嘉定校区机械楼B418室
电子邮箱：
jbyu@tongji.edu.cn
教育背景：
2009 年4月毕业于上海交通大学机械工程专业，博士
2005 年4月毕业于上海大学机械工程专业， 硕士
2002 年7月毕业于浙江工业大学工业工程专业，本科

工作履历：
同济大学机械能源学院 2013.10-
上海大学 机械自动化学院 2009.5-2013.10
2008 年赴美国辛辛那提大学智能维护系统产学研究中心 国外访问****

学术兼职：
目前担任IEEE Transactions on Instrumentation and Measurement的Associate Editor，Advances in Mechanical Engineering (2014-2019，SCI检索)，Recent Patents on Mechanical Engineering(EI检索)国际期刊编辑委员会的成员。
研究领域：
智能制造、设备智能预诊维护、先进质量控制、人工智能、生产系统设计优化
在研项目 ：
1. 国家电网公司科学技术项目，基于深度学习技术的智慧审计研究，2020年1月 至2020年12月，65万，主持，在研。
2.中央高校基本科研业务费-中青年科技领军人才支持计划, 设备智能维护与制造过程控制, 2019年12月-2022年12月，30万，在研，主持；
3. 上海科委“科技创新行动计划”高新技术领域项目，基于机器视觉的空间精密导电滑环在线监测与控制系统研发及示范应用，** ,2019年6月-2021年6月，30万，在研，主持；
4. 中央高校基本科研业务费-2019年人工智能领域学科交叉重大项目，人工智能驱动的机械装备预测运行与精准服务技术，2019年9月-2021年9月，60万，在研，主持；
5.国家自然科学基金(**)，基于机器视觉的晶圆表面缺陷量化探测与辨识研究，2018/01-2021/12，49万（直接经费），在研，主持。
6. 上海市科委科技创新行动计划，**，面向军用特种车制孔过程的制孔质量智能控制技术，2017.7-2019.6, 18万，结题，主持。
7. 中央高校基本科研业务费学科交叉-面上类项目，大规模半导体制造环境下晶圆表面质量智能控制研究，2018年1月-2019年12月，20万，在研，主持；
8. 军工项目，导电滑环热力电建模与工艺优化研究，2017年7月-2019年5月，50万，结题，主持；
9. 上海航天科技创新基金（重点项目），SAST**，基于多Agent的智能车间混合式控制系统设计与开发技术研究，2015年11月-2017年6月，20万，结题，主持；
10. 中央高校基本科研业务费人才项目，设备智能预诊维护，2016年1月-2017年12月，20万，结题，主持；
11. 中央高校基本科研业务费学科交叉-面上类项目，，机械设备健康退化智能预诊与动态预防维护策略联合研究，2015年1月-2016年12月，20万，结题，主持；
12. 国家自然科学基金面上项目，**，机械设备性能退化的流形特征建模与量化评估预测研究，2014/01-2017/12，70万，结题，主持。
13.上海市教育委员会科研创新项目，13YZ002，旋转机械设备性能退化的量化 评估与预测研究，2013/01-2015/12，8万，结题，主持。
14、国家自然科学基金(青年科学基金项目)，**，基于统计学习方法的复 杂多变量制造过程质量的建模与控制研究，2011/01-2013/12，17.7万，已结题， 主持。
15、教育部高等学校博士点基金课题项目，010，复杂多变量制造过程的状态量化监控与故障溯源研究，2011/01-2013/12，3.6万，已结题，主持。
16、机械制造系统工程国家重点实验室开放课题基金，**，复杂制造系统 偏差流的状态空间建模与诊断体系研究，2010/05-2012/05，8万，已结题，主 持。
17、国家质检总局科技计划项目,流动式起重机远程监控系统开发应用研究， 2011/1-2013/12，40万，与山东特种设备检验研究院联合申请（申请人是高校 方面主持人），2010QK245，已结题，主持；
18、无锡市530计划B类项目，设备健康预诊与管理系统，2010/3-2013/12，60 万，已结题，主持；
19、上海市优青专项基金，基于统计学习方法的设备智能预诊维护研究，2010/6-2012/6，6万，已结题，主持；
20、上大创新基金，复杂制造过程的故障源诊断体系研究，2009/9-2011/9，5万，已结题，主持；
21. 企业委托项目，临床路径管理暨医患互动网络平台软件系统，2015年3月至 2016年12月，12万，主持；
22、企业委托项目，建筑工地扬尘远程监控系统，2015/4-2015/7，10万，已结题，主持。
23、企业委托项目，城市亮化远程控制软件系统开发，2014/9-2015/3，13万，主持；
24、企业委托项目，高铁动车组vip座椅控制器单元及车载控制电气系统， 2014/5-2014/12，12.5万，已结题，主持；
25、企业委托项目，高铁动车组vip座椅控制器系统， 2018/5-2018/12，25万，已结题，主持；
26、企业委托项目，流动式起重机远程监控系统开发应用研究，2013/11-2014/3，5万，已结题，主持。

学术成果与奖励：
2014-2019年“生产与制造工程”方向中国高被引****
专著：Jianbo Yu, Statistical learning-based approach for multivariate manufacturing process control, The 11 Chapter of Data Mining for Zero-Defect Manufacturing, Editor: Kesheng Wang and Yi Wang, Tapir Academic Press, 2012 (ISBN: 978-82-519-2776-5).
发明专利与软件版权：
[1] 余建波，尹纪庭，刘美芳，大规模半导体制造过程的监控与故障诊断方法，授权号：CN B，2011年，
[2] 余建波，刘美芳，基于多路传感信息的设备健康状态评估与预测方法，专利申请号：1.X，2011年。
[1]大规模半导体制造过程的监控与故障诊断方法，发明专利，CN B
[2]工程机械远程监控与智能预诊维护系统，软件版权，2016SR026823
[3]面向军用特征车制孔过程的制孔质量智能控制系统，软件版权，2019SR**
[4]空间用滑环测试过程管理与可靠性评估系统，20189年3月，软件版权，2019SR**

论文：
国内外期刊发表的论文
[1] Jianbo Yu*, Jiatong Liu, Two-Dimensional Principal Component Analysis-Based Convolutional Autoencoder for Wafer Map Defect Detection, IEEE Transactions on Industrial Electronics, Accepted in 2020.7.
[2] Jianbo Yu*, Zongli Shen,Xiaoyun Zheng, Joint Feature and Label Adversarial Network for Wafer Map Defect Recognition, IEEE Transactions on Automation Science and Engineering, Accepted in 2020.6.
[3] Jianbo Yu*, Peng Guo, Run-to-Run control of chemical mechanical polishing process based on deep reinforcement learning, IEEE Transactions on Semiconductor Manufacturing, Accepted in 2020.6.
[4] Jianbo Yu*, Xinkang Zhou, One-Dimension Residual Convolutional Auto-Encoder-Based Feature Learning for Gearbox Fault Diagnosis, IEEE Transactions on Industrial Informatics, 16(10), 2020, 6347-6358.
[5] Jianbo Yu* , Chengyi Zhang, Shijin Wang, Multichannel One-dimensional Convolutional Neural Network-based Feature Learning for Fault Diagnosis of Industrial Processes, Neural Computing and Applications, Accepted in 2020.6
[6] Jianbo Yu*, Chenyi Zhang, Manifold Regularized Stacked Auto-encoders-Based Feature Learning for Fault Detection in Industrial Processes, Journal of Process Control, 92, 2020, 119-136.
[7] Jianbo Yu*, Guoliang Liu, Knowledge-based deep belief network for machining roughness prediction and knowledge discovery, Computers In Industry, Accepted in 2020.5.
[8] Jianbo Yu*, Guoliang Liu, Knowledge Extraction and Insertion to Deep Belief Network for Gearbox Fault Diagnosis, Knowledge-Based Systems, 197, 105883 2020.
[9] Chengyi Zhang, Jianbo Yu*, Shijin Wang, Fault Detection and Recognition of Multivariate Process Based on Feature Learning of One-dimensional Convolutional Neural Network and Stacked Denoised Autoencoder, International Journal of Production Research, Accepted in 2.2020.
[10] Jianbo Yu*, Haiqiang Liu, Xiaoyun Zheng, Two-dimensional joint local and nonlocal discriminant analysis-based 2D image feature extraction for deep learning, Neural Computing and Applications, 32, 2020, 6009–6024.
[11] Shumei Chen, Jianbo Yu*, Shijin Wang, Monitoring of Complex Profiles Based on Deep Stacked Denoising Autoencoders, Computers & Industrial Engineering, 143, 2020, 106402.
[12] Shumei Chen, Jianbo Yu*, Shijin Wang One-dimension Convolutional Auto-Encoder-Based Feature Learning for Fault Diagnosis of Multivariate Processes, Journal of Process Control, 87, 2020. 54-67.
[13] Jianbo Yu*, Enhanced Stacked Denoising Autoencoder-Based Feature Learning for Recognition of Wafer Map Defects, IEEE Transactions on Semiconductor Manufacturing, 32(4), 2019, 613-624.
[14] Shumei Chen, Jianbo Yu*, Deep recurrent neural network-based residual control chart for autocorrelated processes, Quality and Reliability Engineering International, 35(8), 2019, 2687-2708.
[15] Jianbo Yu, Manifold Regularized Stacked Denoising Autoencoders with Feature Selection, Neurocomputing, 358, 2019, 235-245.
[16] Jianbo Yu*, Xiaoyun Zheng, Shijin Wang, A deep autoencoder feature learning method for process pattern recognition, Journal of Process Control,79, 1-15, 2019.
[17] Jianbo Yu*, Xiaoyun Zheng, Stacked convolutional sparse denoising auto-encoder for identification of defect patterns in semiconductor wafer map, Computers in Industry, 109. 121-133. Aug. 2019.
[18] Jianbo Yu*, Evolutionary Manifold Regularized Stacked Denoising Autoencoders for Gearbox Fault Diagnosis, Knowledge-Based Systems, 178, 111-122. 2019.
[19] Jianbo Yu*, Tianzhong Hu, Haiqiang Liu, A New Morphological Filter for Fault Feature Extraction of Vibration Signals, IEEE Access, 7, 53743-53753, 2019
[20] Jianbo Yu*, Peng Zhu, Weighted self-regulation complex network-based variation modeling and error source diagnosis of Hybrid Multistage Machining Processes, IEEE Access, 7, 36033-36044, 2019.
[21] Jianbo Yu*, A selective deep stacked denoising autoencoders ensemble with negative correlation learning for gearbox fault diagnosis, Computers in Industry, 108, 2019, 62-72.
[22] Jianbo Yu*, Xiaoyun Zheng, Shijing Wang, Stacked Denoising Autoencoder-Based Feature Learning for Out-of-control Source Recognition in Multivariate Manufacturing Process, Quality and Reliability Engineering International, 35(1). 2019, 204-223.
[23] Jianbo Yu*, State of Health Prediction of Lithium-Ion Batteries: Multiscale Logic Regression and Gaussian Process Regression Ensemble, Reliability Engineering & System Safety,174, Jun. 2018, 82-95.
[24] Jianbo Yu*, Tool Condition Prognostics Using Logistic Regression with Penalization and Manifold Regularization, Applied Soft Computing, 64, 2018, 454-467.
[25] Jianbo Yu*, Haiqiang Liu, Sparse coding shrinkage in intrinsic time scale decomposition for weak fault feature extraction of bearings, IEEE Transactions on Instrumentation and Measurement, 67(7), 1579-1592., 2018.
[26] Jingxiang Lv; Jianbo Yu*, Average combination difference morphological filters for fault feature extraction of bearing, Mechanical Systems and Signal Processing, 100, 827-845, 2018.
[27] Jianbo Yu*, Jingxiang Lv, Weak fault feature extraction of rolling bearings using local mean decomposition-based multilayer hybrid denoising, IEEE Transactions on Instrumentation and Measurement, 66(12), 3148-3159, 2017.
[28] Jianbo Yu, Aircraft Engine Health Prognostics Based on Logistic Regression with Penalization Regularization and State-Space-Based Degradation Framework, Aerospace Science and Technology, 68, 345-361, 2017.
[29] Jianbo Yu, Adaptive Hidden Markov model-based online learning framework for bearing faulty detection and performance degradation monitoring, Mechanical Systems and Signal Processing, 83, 149-162, Jan. 2017.
[30] Jianbo Yu, Machinery Fault diagnosis using joint global and local/nonlocal discriminant analysis with selective ensemble learning, Journal of Sound and Vibration, 382, Nov.2016, 340-356.
[31] Jianbo Yu, Process monitoring through manifold regularization-based GMM with global/local information, Journal of Process Control, 45, 84-99, Sep. 2016.
[32] Jianbo Yu, Lu Xiaolei, Wafer map defect detection and recognition using joint local and nonlocal linear discriminant analysis, IEEE Transactions on Semiconductor Manufacturing, 29(1), 00.33-43, Feb. 2016.
[33] Jianbo Yu, Machine health prognostics using Bayesian-inference-based probabilistic indication and high-order particle filtering framework, Journal of Sound and Vibration, 358(8), pp.97-110, Dec. 2015.
[34] Jianbo Yu, State-of-Health Monitoring and Prediction of Lithium-Ion Battery Using Probabilistic Indication and State Space Model, IEEE Transactions on Instrumentation and Measurement, 64(11), 2015, pp.2937-2949.
[35] Jianbo Yu, Health degradation detection and monitoring of Lithium-Ion battery based on adaptive learning method, IEEE Transactions on Instrumentation and Measurement, vol.63, no.7, 2014, pp.1709-1721.
[36] Jianbo Yu, A nonlinear probabilistic method and contribution analysis for machine condition monitoring, Mechanical Systems and Signal Processing, 37(1-2), 2013, pp. 293-314.
[37] Jianbo Yu, Local and nonlocal preserving projection for bearing defect classification and performance assessment, IEEE Transactions on Industrial Electronics, vol.59, no.5, 2012, pp. 2363-2376.
[38] Jianbo Yu, Health condition monitoring of machines based on hidden Markov model and contribution analysis, IEEE Transactions on Instrumentation and Measurement, vol.61, no.8, 2012, 2200-2211.
[39] Jianbo Yu, Semiconductor manufacturing process monitoring using Gaussian mixture model and Bayesian method with local and nonlocal information，IEEE Transactions on Semiconductor Manufacturing, vol.25, no.3, 2012, pp. 480-493.
[40] Jianbo Yu, Machine tool condition monitoring based on an adaptive Gaussian mixture model, Journal of Manufacturing Science and Engineering- Transactions of the ASME, vol.134, no.3, 2012, pp. 031004-(1-13pages).
[41] Jianbo Yu, Local and global principal component analysis for process monitoring, Journal of Process Control, vol.22, no.7, 2012, pp.1358-1373.
[42] Jianbo Yu, Gaussian mixture models-based control chart pattern recognition, International Journal of Production Research, vol.50, no.23, 2012, pp.6746-6762.
[43] Jianbo Yu, Fault detection using principal components based Gaussian mixture model for semiconductor manufacturing processes, IEEE Transactions on Semiconductor Manufacturing, vol.24, no.3, 2011, pp.432-444.
[44] Jianbo Yu, Bearing performance degradation assessment using locality preserving projections and Gaussian mixture models, Mechanical Systems and Signal Processing, vol.25, no.7, 2011, 2573-2588.
[45] Jianbo Yu, A hybrid feature selection scheme and self-organizing map model for machine health assessment, Applied Soft Computing, vol.11, no.5, 2011, pp.4041-4054.
[46] Jianbo Yu, Bearing performance degradation assessment using locality preserving projections, Expert Systems with Applications, vol.38, no.6, 2011, pp.7440-7450.
[47] Jianbo Yu, Online tool wear prediction in drilling operations using selective artificial neural network ensemble model, Neural Computing & Applications, vol.20, no.4, 2011, pp.473-485.
[48] Jianbo Yu, Meifang Liu, Hao Wu, Local preserving projections-based feature selection and Gaussian mixture model for machine health assessment, Proceedings of the Institution of Mechanical Engineers, Part C, Journal of Mechanical Engineering Science, 2011, vol.225, no.7 pp.1703-1717.
[49] Jianbo Yu, Pattern recognition of manufacturing process signals using Gaussian Mixture models-based recognition system, Computers & Industrial Engineering, vol.61, no.3, 2011, pp. 881-890.
[50] Jianbo Yu, Jianping Liu. LRProb control chart based on logistic regression for monitoring mean shifts of auto-correlated manufacturing processes, International Journal of Production Research, vol.49, no.8, 2011, pp.2301-2326.
[51] Jianbo Yu, Hidden Markov Models Combining Local and Global Information for Nonlinear and Multimodal Process Monitoring, Journal of Process Control, vol.20, no.3, 2010, pp.344-359.
[52] Jianbo Yu, Shijing Wang, Using Minimum Quantization Error Chart for the Monitoring of Process States in Multivariate Manufacturing Processes，Computers & Industrial Engineering, vol.57, no.4, 2009, pp.1300-1312.
[53] Jianbo Yu, Lifeng Xi. A neural network ensemble-based model for on-line monitoring and diagnosis of out-of-control signals in multivariate manufacturing processes. Expert Systems With Applications, vol.36, no.1, 2009, pp.909-921.
[54] Jianbo Yu, Lifeng Xi. A Hybrid Learning-based Model for On-line Monitoring and Diagnosis of Out-of-control Signals in Multivariate Processes, International Journal of Production Research, vol.47, no.15, 2009, pp.4077–4108.
[55] Jianbo Yu, Lifeng Xi, Xiaojun Zhou. Identifying Source(s) of Out-of-control Signals in Multivariate Manufacturing Processes Using Selective Neural Network Ensemble, Engineering Applications of Artificial Intelligence, vol.22, no.1, 2009, pp.141-152.
[56] Jianbo Yu, Lifeng Xi, Xiaojun Zhou. Intelligent Monitoring and Diagnosis of Manufacturing Processes Using an Integrated Approach of KBANN and GA, Computers in Industry. vol.59, no.5, 2008, pp.489-501.
[57] Jianbo Yu, Lifeng Xi. Using MQE Chart Based on Self-Organizing Map (SOM) Neural Network for Monitoring Out-of-control Signals in Manufacturing Processes. International Journal of Production Research, vol.46, no.21, 2008, pp.5907–5933.
[58] Jianbo Yu, Shijin Wang, Lifeng Xi. Evolving Artificial Neural Networks Using an Improved PSO and DPSO. Neurocomputing, vol.71, no.4-6, 2008, pp.1054-1060. (入选近10年最多引用论文)
[59] Jianbo Yu, Lifeng Xi, Shijin Wang. An Improved Particle Swarm Optimization for Evolving Feedforward Artificial Neural Networks. Neural Processing Letters, vol.26, no.3, 2007, 217-231.
[60] Bin Wu and Jianbo Yu*. A neural network ensemble model for on-line monitoring of process mean and variance shifts in correlated processes, Expert Systems with Applications, vol.37, no. 6, 2010, pp. 4058-4065.
[61] Jianbo Yu, Lifeng Xi. Intelligent monitoring and diagnosis of manufacturing process using an integrated approach of neural network ensemble and genetic algorithm. International Journal of Computer Applications in Technology, vol.33, no.2/3, 2008, pp.109–119.
[62] Shijin Wang, WL Cui, Feng Chu, Jianbo Yu, The Interval Min-max Regret Knapsack Packing-delivery Problem，International Journal of Production Research, Accepted in 2020.6
[63] S Wang, R Wu, F Chu, J Yu, Variable neighborhood search-based methods for integrated hybrid flow shop scheduling with distribution, Soft Computing, 1-20, 2019.
[64] Wang, S., Wu, R., Chu, F., Yu, J. Identical parallel machine scheduling with assurance of maximum waiting time for an emergency job. Computers & Operations Research, 118, 104918, 2020.
[65] Shijin Wang, Xiaodong Wang, Jianbo Yu, Shuan Ma, Ming Liu Bi-objective identical parallel machine scheduling to minimize total energy consumption and makespan，Journal of Cleaner Production, 193, 2018, 424-440
[66] Wang, Shijin; Wang, Xiaodong; Chu, Feng; Yu, Jianbo, An Energy-efficient Two-stage Hybrid Flow Shop Scheduling Problem in a Glass Production, International Journal of Production Research, Accepted in May. 2019.
[67]Shijin Wang, Xiaodong Wang, Xin Liu, Jianbo Yu. A bi-objective vehicle routing problem with soft time windows and multiple depots to minimize the total energy consumption and customer dissatisfaction. Sustainability, Sustainability, 10(11), 4257, 2018.
[68] Shijin Wang，Jianbo Yu, Edzel Lapira, Jay Lee，A modified support vector data description based novelty detection approach for machinery components，Applied Soft Computing, vol.13, no.2, 2013, pp.1193-1205.
[69] Shijin Wang and Jianbo Yu, An effective heuristic for flexible job-shop scheduling problem with maintenance activities, Computers & Industrial Engineering，vol.59, no.3, October 2010, pp.436-447.
[70] Tianyi Wang, Jianbo Yu, Siegel, D., and Lee, J. A similarity-based prognostics approach for remaining useful life estimation of engineered systems，Prognostics and Health Management, 2008. PHM 2008. International Conference on, Denver, CO，6-9 Oct. 2008，pp.1–6.
[71] Jianbo Yu, Lifeng Xi. A Neural Network Ensemble Approach for the Recognition of SPC Chart Patterns. Natural Computation, 2007. ICNC 2007. Third International Conference on, 2, 24-27 Aug. 2007:575 - 579.
[72] Jianbo Yu, Lifeng Xi. A Neural Network Ensemble for Classifying Source(s) in Multivariate Manufacturing Processes, 2007 IEEE International Conference on Industrial Engineering and Engineering Management, 2007, 1246-1250.
[73] J. Yu, A review for manifold learning-based statistical process Control, 11th International Symposium on Measurement and Quality Control, Cracow and Kielce, POLAND, 2013, 1-3.
[74] Xiaoyun Zheng; Jian-bo Yu. Multivariate process monitoring and fault identification using Convolutional Neural Networks. In: Proceeding of the 24th International Conference on Industrial Engineering and Engineering Management 2018. Springer, Singapore, 2019.1, p. 197-208.
[75] Shu-mei Chen; Jian-bo Yu. Mean shifts monitoring of auto-correlated manufacturing processes using long short-term memory-based recurrent neural network. In: Proceeding of the 24th International Conference on Industrial Engineering and Engineering Management 2018. Springer, Singapore, 2019. 1,p. 83-94.
[76] Peng Zhu, Jian-bo Yu. Weighted self-regulation complex network-based modeling and key nodes identification of multistage assembling process. In: Proceeding of the 24th International Conference on Industrial Engineering and Engineering Management 2018. Springer, Singapore, 2019.1, p. 410-418.
[77] Guo Peng, Jianbo Yu, Optimal Control of Blank Holder Force Based on Deep Reinforcement Learning, In: Proceeding of the 25th International Conference on Industrial Engineering and Engineering Management 2019. Springer, Macau, 2019. p. 410-418.
[78] Zongli Shen, Jianbo Yu, Wafer Map Defect Recognition Based on Deep Transfer Learning, In: Proceeding of the 25th International Conference on Industrial Engineering and Engineering Management 2019. Springer, Macau, 2019. p. 410-418.
[79] 周俊杰、余建波*，基于机器视觉的加工刀具磨损量在线测量，上海交通大学学报，2020.7录用。
[80] 刘国梁，余建波*，堆叠降噪自编码器的神经符号模型及在晶圆表面缺陷识别，自动化学报，2020.5录用。
[81] 叶壮，余建波*，基于多通道加权卷积神经网络的齿轮箱振动信号特征提取，机械工程学报，2020.4，录用.
[82] 郭鹏，余建波*，基于深度强化学习的制造过程Run-to-Run控制，自动化学报，2020.2录用。
[83] 孙远航，王永松，孙习武，刘贤军，余建波*， 航天信号传输关键部件失效建模与工艺优化研究，机械工程学报，2019.12，录用.
[84] 余建波*, 沈宗礼, 基于深度域对抗迁移学习的轴承故障诊断，机械工程学报，2019.12，录用.
[85] 郭鹏,张新艳,余建波*, 基于深度强化学习与有限元仿真集成的拉深成形优化控制，机械工程学报，2019.12，录用.
[86] 刘珈彤，余建波*，基于二维主成分分析卷积自编码器的晶圆表面缺陷模式识别，计算机辅助设计及图形学学报，2019.10录用
[87] 刘国梁，余建波*，知识堆叠滤噪编码器，自动化学报，2019.8录用。
[88] 周兴康，余建波*，基于一维残差卷积自编码器的齿轮箱故障诊断，机械工程学报，2019.06，录用
[89] 张新艳，郭鹏，余建波*，基于深度强化学习的压边力优化控制，哈尔滨工业大学学报，2019.10录用。
[90] 叶壮，余建波*， 基于多通道一维卷积神经网络特征学习的齿轮箱故障诊断方法，振动与冲击，2019.9录用
[91] 孙远航，余建波*，刘贤军，孙习武，王永松，基于导电滑环磨损失效模型的小子样可靠性评估，宇航学报，2019.9录用
[92] 陈淑梅，余建波*，卷积神经网络多变量过程特征学习与故障诊断，哈尔滨工业大学学报，2019.8录用
[93] 宗礼，余建波*，基于迁移学习与深度森林的晶圆图缺陷识别，浙江大学学报，2019.8录用。
[94] 韩笑乐，胡天中，余建波*，基于自适应加权多尺度组合形态滤波的轴承故障特征提取研究, 振动与冲击, 39（1），2020，245-252
[95] 刘国梁，余建波*，基于知识深度置信网络的加工粗糙度预测，机械工程学报，55（20），2019， 94-106
[96] 祝鹏，余建波*，郑小云，孙习武，王永松，陈长江，混合机械加工过程的偏差网络建模与误差溯源，计算机集成制造，2018.10录用
[97] 刘贤军、孙远航、王永松、施英莹、孙习武、余建波*，基于多场耦合建模与Bootstrap方法的滑环可靠性评估，北京航空航天大学学报，45（11），2019，2301-2311
[98] 董晨阳，郑小云，余建波*，基于过程挖掘与复杂网络集成的制造过程资源 建模与关键加工节点识别，机械工程学报，2019，55 (3): 169-180
[99] 胡天中，余建波*，基于多尺度分解和深度学习的锂电池寿命预测，浙江大学学报，53（10），2019，1-13
[100] 祝鹏，余建波*，郑小云，王永松，孙习武，机械装配过程的偏差传递网络建模与误差溯源，浙江大学学报，2019，53，1582-1593
[101] 刘海强，余建波*，二维局部均值分解算法，计算机辅助设计及图形学学报，10，2018，1859-1869.
[102] 郑小云，余建波*，刘海强，程辉，孙习武，吴昊，混合式多阶段加工过程的自适应加权偏差传递网络建模与分析，机械工程学报，54(13), 2018, 179-191.
[103] 余建波，郑小云，李传锋，董晨阳，基于过程挖掘的临床路径Petri网建模研究，同济大学学报，46(4), 524-533, 2018
[104] 余建波;董晨阳;李传锋;程辉;孙习武，基于统计α算法的过程挖掘研究，北京航空航天大学学报，44, 895-906,2018 1008-973X
[105] 余建波，李传锋，吕靖香，轴承故障信号的平均组合差值形态滤波分析, 浙大学报，52(10), 1845-1853,2018
[106] 余建波*，吕靖香，程辉，孙习武，吴昊，基于ITD和改进形态滤波的滚动轴承故障诊断，北京航空航天大学学报，2018，44（2），241-249.
[107] 吕靖香，余建波*，基于多层混合滤噪的轴承早期弱故障特征提取方法，振动与冲击，2018，37（8），22-27.
[108] 余建波*;刘海强;郑小云;周炳海;程辉;孙习武，基于ITD与稀疏编码收缩的滚动轴承故障特征提取方法，振动与冲击，37(9),2018, 23-29
[109] 卢笑蕾，余建波*，基于混合模型与流形调节的晶圆表面缺陷识别，计算机集成制造，24（2），2018，302-308.
[110] 余建波*，董晨阳，李传锋，刘海强，基于统计α算法的临床路径过程挖掘研究, 浙江大学学报，2017，51（10），1881-1890
[111]余建波*，宗卫周，程辉，基于CSMA/CA的电力载波通信及在照明系统应用，东北大学学报，38（6），2017,766-771。
[112] 余建波*，卢笑蕾，宗卫周，基于局部与非局部线性判别分析和高斯混合模型动态集成的晶圆表面缺陷探测与识别，自动化学报，42(1), 2016, 47-59.
[113] 陈思汉，余建波*，基于二维局部均值分解的图像多尺度分析处理，计算机辅助设计及图形学学报，27（10），1842-1850，2015.
[114] 陈思汉，余建波*，基于二维局部均值分解的自适应保真项全变分图像滤噪算法，计算机辅助设计及图形学学报，28(6)，2016, 986-994.
[115] 刘美芳, 尹纪庭, 余建波*, 基于贝叶斯推论和自组织映射的轴承性能退化评估方法, 计算机集成制造系统，v.18, no.10, 2012, pp.2237-2244.
[116] 陈思汉，余建波*，基于二维局部均值分解的图像边缘检测算法，计算机科学与探索，10(6), 2016, 847-855.
[117] 杨梅，陈思汉，吴昊，余建波，LMD滤噪算法及在旋转机械转子故障诊断中的应用，噪声与振动控制，2015 vol.35, no.2, 2015, 160-164.
[118] 刘美芳,尹纪庭, 余建波*, 基于SOA的工程机械远程智能预诊维护系统研究，中国机械工程. Vol.23, no.19, 2012, pp.2320-2326.
[119] 吴斌，余建波，奚立峰，周炳海, 智能重构制造控制系统集成框架, 计算机集成制造系统, vol.14, no.1, 2008, 73-78.
[120] 尹纪庭，袁佳，余建波*, 智能家居系统研究综述, 中国科技论文在线, 2012, 1-9.
[121] 尹纪庭，袁佳，余建波*. LED景观灯照明智能控制系统. 计算机工程, 2013, 39(9): 317-320.
[122] 尹纪庭, 袁佳，焦志曼，吴斌，张在房，余建波*, 基于ARM和Zigbee的智能家居控制系统研究与开发, 计算机测量与控制, 2013, 21（9）, 2451-2454.
[123] 袁佳，焦志曼，余建波*，LED节能照明智能控制系统综述, 中国科技论文在线, 2013, 1-13.
[124] 杨梅，陈思汉，余建波*，旋转机械故障智能诊断系统研究, 中国科技论文在线, 2014, 1-14.
[125] 袁佳，焦志曼，余建波*，基于Internet和ZigBee的制造车间分布式远程监测系统，机械制造，2014年第52卷第8期，70-74
[126] 焦志曼，袁佳，余建波*，面向网络化车间制造的工序质量智能控制系统，机械制造, 2014年第52卷第6期，1-5.
[127] 余建波，李传峰，吴昊，程辉, 基于自组织混合模型的多变量过程控制方法，《上海航天》， 2016, 33(5):42-49。
[128]余建波，宗卫周，王涛、张国栋, 程辉，城市智慧照明控制系统研究与实现，《计算机工程与设计》，录用，2016.12。
[129] 宗卫周，余建波*，基于天空模型的公共场所照明智能控制，计算机测量与控制，25(8)，2017.3，50-53。
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