主要成就
在設備智慧型預診維護與可靠性、複雜製造過程質量控制、機器學習、生產系統設計最佳化等研究領域，申請國家專利4項(授權1項)，發表英文專著一章(負責第11章)，已在國內外學術期刊(包括IEEE/ASME Trans系列著名期刊長文6篇)上發表學術論文近40篇，其中以第一作者(或通訊作者)身份在SCI 源期刊上發表26篇論文，發表的學術論文已經被國內外同行引用總計近570次(其中SCI期刊他超250次)，單篇最高引用超85次。入選愛思唯爾發布2015年中國高被引學者（Most Cited Chinese Researchers）工業與製造部第八名 。錄用或發表的重要國際期刊包括：《IEEE Transactions on Industrial Electronics》、《IEEE Transactions on Semiconductor Manufacturing》、《IEEE Transactions on Instrumentation and Measurement》、《Journal of Manufacturing Science and Engineering-Transactions of the ASME》、《Mechanical Systems and Signal Processing》、《Journal of Process Control》、《Applied Soft Computing》、《Computers In Industry》等。
代表論文
[1] Jianbo Yu, Process monitoring through manifold regularization-based GMM with global/local information, Journal of Process Control, 45, 84-99, Sep. 2016.
[2] 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.
[3] Jianbo Yu, Adaptive Hidden Markov model-based online learning framework for bearing faulty detection and performance degradation monitoring, Mechanical Systems and Signal Processing, 83, 2017, 149.162.
[4] 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.
[5] 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.
[6] 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.
[7] 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.
[8] 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.
[9] 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.
[10] 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.
[11] 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.
[12] 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).
[13] Jianbo Yu, Local and global principal component analysis for process monitoring, Journal of Process Control, vol.22, no.7, 2012, pp.1358-1373.
[14] Jianbo Yu, Gaussian mixture models-based control chart pattern recognition, International Journal of Production Research, vol.50, no.23, 2012, pp.6746-6762.
[15] 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.
[16] 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.
[17] 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.
[18] Jianbo Yu, Bearing performance degradation assessment using locality preserving projections, Expert Systems with Applications, vol.38, no.6, 2011, pp.7440-7450.
[19] 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.
[20] 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.
[21] 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.
[22] 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.
[23] 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.
[24] 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.
[25] 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.
[26] 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.
[27] 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.
[28] 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.
[29] 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.
[30] 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. (Most Cited Neurocomputing Articles)
[31] 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.
[32] 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.
[33] 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.
[34] 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
[35] 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.
[36] 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.
[37] 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.
[38] 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.
[39] 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.
[40] 盧笑蕾，余建波*，基於混合模型與流形調節的晶圓表面缺陷識別，計算機集成製造，42(1)，2016, 47-59.
[41] 余建波*，盧笑蕾，宗衛周，基於局部與非局部線性判別分析和高斯混合模型動態集成的晶圓表面缺陷探測與識別，自動化學報，42(1), Jan.2016, 47-59.
[] 吳斌，盧笑蕾，余建波 ，晶圓表面缺陷模式的線上探測與自適應識別研究，計算機工程與套用，2016年 52(17), 261-266.
[42] 陳思漢，余建波*，基於二維局部均值分解的圖像分析處理，計算機輔助設計及圖形學學報，27（10），1842-1850，2015.
[43] 陳思漢，余建波*，基於二維局部均值分解的自適應保真項全變分圖像濾噪方法，計算機輔助設計及圖形學學報，28(6), 2016, 986-994.
[44] 陳思漢，余建波*，基於二維局部均值分解的圖像邊緣檢測算法，計算機科學與探索，10(6), 2016, 847-855.
[45] 楊梅，陳思漢，吳昊，余建波，LMD濾噪算法及在旋轉機械轉子故障診斷中的套用，噪聲與振動控制，2015 vol.35, no.2, 2015, 160-164.
[46] 劉美芳,尹紀庭, 余建波*, 基於SOA的工程機械遠程智慧型預診維護系統研究，中國機械工程. Vol.23, no.19, 2012, pp.2320-2326.
[47] 劉美芳, 尹紀庭, 余建波*, 基於貝葉斯推論和自組織映射的軸承性能退化評估方法, 計算機集成製造系統，v.18, no.10, 2012, pp.2237-2244.
[48] 吳斌，余建波，奚立峰，周炳海, 智慧型重構製造控制系統集成框架, 計算機集成製造系統, vol.14, no.1, 2008, 73-78.
[49] 尹紀庭，袁佳，余建波*, 智慧型家居系統研究綜述, 中國科技論文線上, 2012, 1-9.
[50] 尹紀庭，袁佳，余建波*. LED景觀燈照明智慧型控制系統. 計算機工程, 2013, 39(9): 317-320.
[51] 尹紀庭, 袁佳，焦志曼，吳斌，張在房，余建波*, 基於ARM和Zigbee的智慧型家居控制系統研究與開發, 計算機測量與控制, 2013, 21（9）, 2451-2454.
[52] 袁佳，焦志曼，余建波*，LED節能照明智慧型控制系統綜述, 中國科技論文線上, 2013, 1-13.
[53] 楊梅，陳思漢，余建波*，旋轉機械故障智慧型診斷系統研究, 中國科技論文線上, 2014, 1-14.
[54] 袁佳，焦志曼，余建波*，基於Internet和ZigBee的製造車間分散式遠程監測系統，機械製造，2014年第52卷第8期，70-74
[55] 焦志曼，袁佳，余建波*，面向網路化車間製造的工序質量智慧型控制系統，機械製造, 2014年第52卷第6期，1-5.
余建波，李傳峰，吳昊，陳輝，基於自組織混合模型的多變數航天產品加工過程控制方法研究，《上海航天》 2016, 33(5):42-49
[56]余建波，宗衛周，程輝《基於CSMA/CA的電力載波並發通訊及在照明控制套用研究》,東北大學學報，已經錄用，2016年1月。
[57]呂靖香；*余建波, 基於多層混合濾噪的軸承早期弱故障特徵提取方法, 振動與衝擊,2017錄用
專著：
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).
主持項目：
(1)國家自然科學基金(面上項目)，“機械設備性能退化的流形特徵建模與量化評估預測研究”，(2014年1月-2017年12月，編號：51375290，70萬，余建波，項目主持人);
(2)國家自然科學基金(青年項目)，“基於統計學習方法的複雜多變數製造過程質量的建模與控制研究”，(2011年1月-2013年12月，編號：71001060，17.7萬，余建波，項目主持人);
(3)上海市教育委員會科研創新項目：“旋轉機械設備性能退化的量化評估與預測研究” (2013年1月-2015年12月，編號：13YZ002，8萬，余建波，項目主持人);
(4)企業委託項目，“流動式起重機遠程監控系統開發套用研究”，山東省特種設備檢驗研究院，(2013年11月5日 至 2014年3月30日，5萬，余建波，項目主持人);
(5)企業委託項目，高鐵動車組vip座椅控制器單元及車載控制電氣系統，(2014年5月1日 至 2014年12月30日，12.5萬，余建波，項目主持人);
(6)教育部高等學校博士點基金課題項目：“複雜多變數製造過程的狀態量化監控與故障溯源研究”，(2011年1月-2013年12月，編號： 20103108120010 ，3.6萬，余建波，項目主持人);
(7)機械製造系統工程國家重點實驗室開放課題基金，“複雜製造系統質量建模與診斷體系研究”，(2010年5月-2012年5月，編號：2010008，8萬，余建波，項目主持人，已結題);
(8)無錫530創新創業基金B類項目，“設備健康預診與管理系統”，(2010年3月-2013年3月，60萬，余建波，項目主持人，已結題);
(9)慈谿市上林英才創新創業基金B類項目，“嵌入式電子系統與設備智慧型維護系統”，(2013年3月-2016年3月，50萬，余建波，項目主持人);
(10)上海市優青專項基金，“基於統計學習方法的設備智慧型預診維護研究”，(2010年6月-2012年6月，6萬，余建波，項目主持人，已結題);
(11)上海大學創新基金，“基於智慧型學習的製造過程質量控制研究”，(2009年9月-2011年9月，5萬，余建波，項目主持人，已結題);
國家發明專利：
[1] 余建波，尹紀庭，劉美芳，大規模半導體製造過程的監控與故障診斷方法，授權號：CN 102361014 B，2011年，
[2] 余建波，劉美芳，基於多路感測信息的設備健康狀態評估與預測方法，專利申請號：201110171401.X，2011年，
[3] 余建波，尹紀庭，袁佳，城市LED照明控制系統，申請號201210366041.3，2012年，
[4] 尹紀庭，余建波，王小樂，一種LED智慧型調光裝置及方法，申請號201210186556.5，2012年，
[5] 尹紀庭，余建波，基於Internet和Zigbee的智慧型家居控制系統，申請號201210448871.0，2012年。
