2012.09-2018.12：上海交通大学，机械电子工程专业，博士（硕博连读，导师：刘成良教授）
2008.09-2012.06：西南交通大学，机械设计制造及其自动化专业，学士

2021.03 - 至今： 上海交通大学，机械与动力工程学院，机电控制与物流装备研究所，助理教授，硕士/博士生导师
2019.01 - 2021.02： 上海交通大学，机械与动力工程学院，博士后（合作导师：刘成良教授）

机器健康与智能运维：
以重大装备(盾构机/TBM等)、智能制造装备（工业机器人/数控机床）为研究对象，基于复杂系统动力学、信号处理、深度学习、大数据分析、边缘计算、互联网云平台等理论和技术，开展智能感知、模型算法及运维云平台相关研究，主要包括：

1. 信号处理、深度学习及大数据挖掘理论与方法
2. 重大装备(盾构机/TBM等)健康评估与智能运维
3. 智能制造装备故障诊断、健康评估与智能运维
4. 复杂机-电-液系统动力学建模/分析/设计与控制

欢迎对科研有热情的硕/博士生和博士后联系交流！
******每年有1个博士和2个硕士招生名额********

2021.01-2023.12：国家自然科学基金青年项目"柔性约束多变孔位机器人制孔系统耦合振动机理及智能抑振研究(52005326)"，项目负责人
2019.12-2022.11：国家重点研究计划子课题“地下工程装备全生命周期性能预测与优化技术(2019YFB1705203)”，子课题负责人
2020.11-2023.10：国家重点研究计划子课题“农机故障预警与备件精准预测技术研究(2020YFB1709604)”，子课题主要负责人
2019.11-2021.02：中国博士后科学基金面上项目“防空战车弱刚度机器人钻削系统耦合颤振机理与智能抑制(2019M661496)”，项目负责人
2019.01-2021.02：上海市“超级博士后”激励计划“健康评估、故障诊断、预测维护技术”，项目负责人
2022.04-2024.03：上海市自然科学基金面上项目"大型复杂孔位结构件机器人钻削系统耦合颤振机理及时滞调控研究(22ZR1432600)"，项目负责人
2021.01-2022.12：国家重点实验室自主课题“基于运行状态大数据的掘进机环境-载荷-性能预测及掘进参数优化研究(MSVZD202103)”，项目负责人
2021.03-2024.12：上海交通大学新进青年教师科研启动基金，项目负责人
2021.12-2022.12：企业合作项目“大型动力平台智能控制单元技术开发”，项目负责人
2021.01-2024.12：上海市人工智能重大专项“机器智能(2021SHZDZX0102)”，主要负责人
2020.01-2024.12：国家自然科学基金重点项目“超高速电梯动力学设计及响应控制（51935007）”，参与
2019.06-2023.05：国家重点研发计划项目“全断面隧道掘进装备运行服务平台及智能终端研制（2018YFB1702503）”，参与
2021.01-2024.12：国家自然科学基金面上项目“类矩形盾构环臂式电液伺服管片拼装机振动机理及其预测控制方法（52075320）”，参与
2014.01-2017.12：国家自然科学基金面上项目“单向变转速比例泵控非对称液压缸高精度位置控制方法研究(51375297)”，参与
2013.01-2017.12：国家973课题“液压推进系统的高刚度设计及振动能量耗吸技术（2013CB035403）”，参与
2017.12-2019.12：上海市科学技术委员会科研计划项目“基于CPS 的军用特种车智能制孔机器人系统及示范应用（17511109200）”， 参与
2015.01-2017.12：企业重大合作项目“矩形盾构液压拼装机器人智能化关键技术研究（2015-SK-01-02）”，参与
2014.12-2017.12：企业重大合作项目“机器人制孔钻削颤振分析及切削加工稳定性方法（SAMC14-JS-15-046）”，参与

在Cell子刊iScience, Mechanical Systems and Signal Processing, Knowledge-Based Systems, Automation in Construction, Journal of Intelligent Manufacturing, Science China Technological Sciences, IEEE Trans.等国内外期刊上发表SCI论文56篇（1篇入选ESI热点论文，4篇入选ESI高被引论文，中科院1区top18篇，中科院1/2区39篇，JCR Q1/2区52篇，*为通讯作者）, 在Google Scholar‬上被引用近1100次, h-index=21。

2022
[78] Qin C., Shi G., Tao J., Yu H., Jin Y., Xiao D., Zhang Z., Liu C., An adaptive hierarchical decomposition-based method for multi-step cutterhead torque forecast of shield machine. Mechanical Systems and Signal Processing, 2022, 175:109148. (SCI, IF: 8.934)
[77] Liu Y., Qin C.*,et al, Diagnosis for MHCD with deep learning. iScience, 2022, Uuder Revision. (SCI, IF: 6.107)
[76] Qin C., Xiao D.,Tao J., Yu H., Jin Y., Sun Y., Liu C., Concentrated velocity synchronous linear chirplet transform with application to robotic drilling chatter monitoring. Measurement, 2022, 194:111090. https://doi.org/10.1016/j.measurement.2022.111090. (SCI, IF: 5.131)
[75] Qin C., Shi G., et al, RCLSTMNet: A Residual-Convolutional-LSTM Neural Network for Forecasting Cutterhead Torque in Shield Machine, International Journal of Control, Automation and Systems, 2022, Uuder Revision. (SCI, IF: 2.964)
[74] Liu Y., Qin C.*,et al, An efficient neural network-based method for patient-specific information involved arrhythmia detection. Knowledge-Based System, 2022, 250:109021. https://doi.org/10.1016/j.knosys.2022.109021. (SCI, IF: 8.139)
[73] Jin Y., Qin C.*, ,et al, A multi-scale CNN with anti-strong-noise and noise-domain adaptation for bearing compound fault diagnosis. Science China Technological Sciences, 2022, https://doi.org/10.1007/s11431-022-2109-4. (SCI, IF: 3.903)
[72] Fu X., Tao J., Qin C., ,et al, , A roller state-based fault diagnosis method for TBM main bearing using two-stream CNN with multi-channel detrending inputs. IEEE Transactions on Instrumentation & Measurement, 2022, Uuder Revision. (SCI, IF:5.332)
[71] Jin Y., Li Z., Qin C.*, et al, A novel interpretable method based on attentional deep neural network for actual ECG quality assessment. Biomedical Signal Processing and Control, 2022, https://doi.org/10.1016/j.bspc.2022.104064. (SCI, IF: 5.076)
[70] Jin Y., Li Z., Liu Y., Liu J., Qin C.*, Zhao L., Liu C*, Multi-class 12-lead ECG Automatic Diagnosis based on a novel Subdomain Adaptive Deep Network. Science China Technological Sciences, 2022, https://doi.org/10.1007/s11431-022-2080-6. (SCI, IF: 3.903)
[69] Tao J., Yu H., Qin C., Sun H., Liu C., A gene expression programming-based method for real-time wear estimation of disc cutter on TBM cutterhead". Neural Computing and Applications, 2022, https://doi.org/10.1007/s00521-022-07597-4. (SCI, IF: 5.102)
[68] Liu C., Ma X. Shi X., Han Y., Qin C., Hu S., A Novel NSCNN for Domain Generalization DiagnosisTasks Across Different Transducers and Different Bearings Specifications. Measurement, 2022, Uuder Revision. (SCI, IF: 5.131)
[67] Liu J., Jin Y., Liu Y, Li Z., Qin C., Chen X, Zhao L; Liu C., A novel P-QRS-T wave localization method in ECG Signals based on HNN. Computers in Biology and Medicine, 2022, Uuder Revision. (SCI, IF: 6.698)
[66] 刘明阳, 陶建峰, 覃程锦, 余宏淦, 刘成良. 基于随机森林与粒子群算法的隧道掘进机操作参数地质类型自适应决策.中南大学学报，2022，录用.
[65] 陶治同，陶建峰，覃程锦，刘成良. 基于时间冲击最优的TBM换刀机器人轨迹规划. 浙江大学学报(工学版)，2022，录用.

2021
[64] Qin C., Shi G., Tao J., Yu H., Jin Y., Lei J., Liu C., Precise cutterhead torque prediction for shield tunneling machines using a novel hybrid deep neural network. Mechanical Systems and Signal Processing, 2021, 151: 107386. (SCI, IF: 8.934, 入选ESI高被引论文)
[63] Qin C., Zeng H., Tao J., Xiao D., Yu H., Sun Y., Liu C., A chatter recognition approach for robotic drilling operations based on SCT. Mechanical Systems and Signal Processing, 2021, Revision submitted. (SCI, IF: 8.934)
[62] Qin C., Jin Y., Tao J., Xiao D.,Yu H., Liu C., Shi G.,Lei J., Liu C., DTCNNMI: A deep twin convolutional neural networks with multi-domain inputs for strongly noisy diesel engine misfire detection. Measurement, 2021, 180: 109548. (SCI, IF: 5.131, 入选ESI热点论文和高被引论文)
[61] Jin Y., Qin C.*, Tao J., Liu C., An accurate and adaptative cutterhead torque prediction method for shield tunneling machines via adaptative residual long-short term memory network. Mechanical Systems and Signal Processing, 2021. https://doi.org/10.1016/j.ymssp.2021.108312. (SCI, IF: 8.934)
[60] Yu H., Tao J.*, Qin C.*, Liu M., Xiao D., Sun H., Liu C., A novel constrained dense convolutional autoencoder and DNN-based semi-supervised method for shield machine tunnel geological formation recognition. Mechanical Systems and Signal Processing, 2021. https://doi.org/10.1016/j.ymssp.2021.108353. (SCI, IF: 8.934)
[59] Shi G., Qin C.*, Tao J., Liu C., A VMD-EWT-LSTM-based multi-step prediction approach for shield tunneling machine cutterhead torque. Knowledge-Based System, 2021, 228:107213. (SCI, IF: 8.139)
[58] Xiao D., Qin C., Ge J., Xia P., Huang Y.*, Liu C., Self-attention-based Adaptive RUL Prediction for IGBT with MCD. Knowledge-Based System, 2022, 239:107902. https://doi.org/10.1016/j.knosys.2021.107902. (SCI, IF: 8.139)
[57] Xiao D., Qin C., Xia P., Jin Y., Huang Y.*，Liu C., Unsupervised DA RUL Prediction for IGBTs under DWC. IEEE Transactions on Power Electronics, 2021, Under Revision. (SCI, IF: 6.153)
[56] Jin Y., Qin C.*, Liu J., Li Z., Shi H., Lin K., Liu Y., Liu C.*, A novel incremental and interactive method for actual heartbeat classification with limited additional labeled samples. IEEE Transactions on Instrumentation & Measurement, 2021, 70: 2507212. (SCI, IF:5.332)
[55] Yu H., Tao J.*, Huang S., Qin C.*, Xiao D., Liu C., A field parameters-based method for real-time wear estimation of disc cutter on TBM cutterhead. Automation in Construction, 2021, 124:103603. (SCI, IF: 10.517)
[54] Jin Y., Liu J., Liu Y., Qin C.*, Li Z., Xiao D., Zhao L., Liu C.*, A novel interpretable method based on dual-Level attentional deep neural network for actual Multi-label Arrhythmia detection. IEEE Transactions on Instrumentation & Measurement, 2022, 71:2500311. https://doi.org/10.1109/TIM.2021.3135330. (SCI, IF:5.332)
[53] Tao J., Qin C.*, Xiong Z., Gao X., Liu C., Optimization and control of cable tensions for hyper-redundant snake arm robots. International Journal of Control, Automation and Systems, 2021, 19: 3764–3775. (SCI, IF: 3.314)
[52] Xiao D., Qin C.*, Yu H., Huang Y.*, Liu C., Zhang J., Unsupervised Machine Fault Diagnosis for Noisy Domain Adaptation using marginal Denoising Autoencoder. Measurement, 2021, 176:109186. (SCI, IF: 5.131)
[51] Jin Y., Qin C.*, Huang Y.*, Liu C., Actual Bearing Compound Fault Diagnosis based on Active Learning and Decoupling Attentional Residual Network. Measurement, 2021, 173: 108500. (SCI, IF: 5.131, 入选ESI高被引论文)
[50] Yu H., Tao J.*, Qin C.*, Xiao D., Sun H., Liu C.，Rock mass type prediction for tunnel boring machine using a novel semi-supervised method. Measurement, 2021, 179: 10954. (SCI, IF: 5.131)
[49] Liu Y., Jin Y., Liu J., Qin C.*, Lin K., Shi H., Tao J., Zhao L., Liu C.*, Precise and efficient heartbeat classification using a novel lightweight-modified method. Biomedical Signal Processing and Control, 2021, 68:102771. (SCI, IF: 5.076)
[48] Xiao D., Qin C.*, Yu H., Huang Y.*，Liu C., Unsupervised deep representation learning for motor fault diagnosis by mutual information maximization. Journal of Intelligent Manufacturing, 2021, 32(2): 377–391. (SCI, IF:7.136)
[47] Liu C., Qin C., Shi X., Wang Z., Zhang G., Han Y., TScatNet: An interpretable cross-domain intelligent diagnosis model with anti-noise and few-shot learning capability. IEEE Transactions on Instrumentation & Measurement, 2021, 70:9279302. (SCI, IF:5.332)
[46] Sun H., Tao J., Qin C., Yu H., Liu C., Dynamics modeling and bifurcation analysis for valve-controlled hydraulic cylinder system containing counterbalance valves. Journal of Vibration Engineering & Technologies, 2021, https://doi.org/10.1007/s42417-021-00342-6. (SCI)
[45] Yu H., Tao J.*, Qin C., Sun H., Liu C., A Novel A-CNN Method for TBM Utilization Factor Estimation. Journal of Physics Conference Series, 2021. https://doi.org/10.1088/1742-6596/2002/1/012049.
[44] Li B., Wang S., Qin C., Gong L, Simulation of Aircraft Anti-skiding Braking System Considering Dynamic Contact Force between Road and Wheels. Journal of Physics Conference Series, 2021, 1905(1):012012.
[43] 刘明阳, 余宏淦，陶建峰, 覃程锦, 刘成良. 基于盾构机运行参数的局部切空间排列与Xgboost融合的岩土类型识别[J].中南大学学报，2022, 53(6): 2080−2091. https://doi.org/10.11817/j.issn.1672-7207.2022.06.010.

2020
[42] Qin C., Tao J., Shi H., Xiao D., Li B., Liu C., A novel Chebyshev-wavelet-based approach for accurate and fast prediction of milling stability. Precision Engineering, 2020, 62:244–255. (SCI, IF:3.315, 入选ESI高被引论文)
[41] Tao J., Qin C.*, Xiao D., Shi H., Ling X., Li B., Liu C., Timely chatter identification for robotic drilling using a local maximum synchrosqueezing-based method. Journal of Intelligent Manufacturing, 2020, 31: 1243–1255. (SCI, IF:7.136)
[40] Jin Y., Qin C.*, Liu J., Lin K., Shi H., Huang Y., Liu C.*, A novel Domain Adaptive Residual Network for automatic Atrial Fibrillation Detection. Knowledge-Based System, 2020, 203:106122. (SCI, IF: 8.139)
[39] Qin C., Tao J., Xiao D., Shi H., Ling X., Liu C., Accurate and efficient stability prediction for milling operations using a Legendre-Chebyshev-based method. International Journal of Advanced Manufacturing Technology, 2020, 107(1–2): 247–258. (SCI, IF:3.563)
[38] Qin C., Tao J., Xiao D., Shi H., Li B., Liu C.. A Legendre wavelet–based stability prediction method for high-speed milling processes. International Journal of Advanced Manufacturing Technology, 2020, 108(7-8): 2397-2408. (SCI, IF:3.563)
[37] Jin Y., Qin C., Huang Y., Zhao W., Liu C., Multi-domain modeling of atrial fibrillation detection with twin attentional convolutional long short-term memory neural networks. Knowledge-Based System, 2020, 193:105460. (SCI, IF: 8.139)
[36] Shi H., Qin C., Xiao D., Zhao L., Liu C., Automated heartbeat classification based on deep neural network with multiple input layers. Knowledge-Based System, 2020, 188:10503. (SCI, IF: 8.139)
[35] Wang H., Shi H., Lin K., Qin C.*, Zhao L., Huang Y., Liu C.*, A high-precision arrhythmia classification method based on dual fully connected neural network. Biomedical Signal Processing and Control, 2020, 58:101874. (SCI, IF: 5.076)
[34] Shi H., Wang H., Qin C., Zhao L., Liu C.. An incremental learning system for atrial fibrillation detection based on transfer learning and active learning. Computer Methods and Programs in Biomedicine, 2020, 187:105219. (SCI, IF: 7.027)
[33] Xiao, D., Tao, Z., Qin C., ...Huang, Y., Liu, C.,Fast Machine Fault Diagnosis Using Marginalized Denoising Autoencoders Based on Acoustic Signal. 2020 Prognostics and Health Management Conference, PHM-Besancon 2020, 2020, pp. 229–234, 9115517.
[32] 蔡道清, 周洪宇, 覃程锦, 李彦明, 刘成良. 基于小波变换的农田图像光照不变特征提取算法[J].农业机械学报,2020,51(02):15-20.
[31] 杨泰春, 陶建峰, 覃程锦, 刘成良. 采用支持向量机的非对称阀控液压缸模型预测控制[J].西安交通大学学报,2020,54(01):93-100+107.

2019
[30] Qin C., Tao J., Liu C., A novel stability prediction method for milling operations using the holistic-interpolation scheme. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 2019, 233(13):4463–4475. (SCI)
[29] Tao J., Qin C.*, Xiao D., Shi H., Liu C., A pre-generated matrix-based method for real-time robotic drilling chatter monitoring. Chinese Journal of Aeronautics, 2019, 32(12): 2755–2764. (SCI, IF: 4.061)
[28] Tao J., Qin C.*, Liu C., A synchroextracting-based method for early chatter identification of robotic drilling process. International Journal of Advanced Manufacturing Technology, 2019, 100(1–4):273–285. (SCI, IF:3.563)
[27] Tao J., Zeng H., Qin C.*, Liu C., Chatter detection in robotic drilling operations combining multi-synchrosqueezing transform and energy entropy. International Journal of Advanced Manufacturing Technology, 2019, 105(7–8): 2879–2890. (SCI, IF:3.563)
[26] Tao J., Qin C.*, Li W., Liu C., Intelligent fault diagnosis of diesel engines via extreme gradient boosting and high-accuracy time–frequency information of vibration signals. Sensors, 2019, 19:3280. (SCI, IF: 3.576)
[25] Xiao D., Huang Y., Zhao L., Qin C., Shi H., Liu C., Domain adaptive motor fault diagnosis using deep transfer learning. IEEE Access, 2019, 7:80937-80949. (SCI, IF: 3.367)
[24] Xiao D., Huang Y., Qin C., Liu Z., Li Y., Liu C., Transfer learning with convolutional neural networks for small sample size problem in machinery fault diagnosis. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 2019, 233(14):5131-5143. (SCI)
[23] Ling X., Tao J., Li B., Qin C., Liu C.. A Multi-physics modeling-based vibration prediction method for switched reluctance motors. Applied Sciences, 2019, 9(21):4544. (SCI, IF: 2.679)
[22] Xiao, D., Huang, Y., Qin C., ...Liu, C., Shan, Z., Health Assessment for Crane Pumps based on Vehicle Tests using Deep Autoencoder and Metric Learning. 2019 IEEE International Conference on Prognostics and Health Management, ICPHM 2019, 2019, 8819387.
[21] Xiao D., Huang Y., Qin C., Shi H., Li Y., Fault Diagnosis of Induction Motors Using Recurrence Quantification Analysis and LSTM with Weighted BN. Shock and Vibration, 2019, 2019:8325218. (SCI)
[20] Shi H., Wang H., Huang Y., Zhao L., Qin C., Liu C., A hierarchical method based on weighted extreme gradient boosting in ECG heartbeat classification. Computer Methods and Programs in Biomedicine, 2019, 171:1-1. (SCI, IF: 5.428)
[19] 段贤强, 陶建峰, 覃程锦, 蔡道清, 李彦明, 刘成良. 变速条件下农业机械路径跟踪稳定控制方法[J].农业机械学报,2019,50(09):18-24+32.
[18] 齐文超, 李彦明, 张锦辉, 覃程锦, 刘成良, 殷月朋. 丘陵山地拖拉机车身调平双闭环模糊PID控制方法[J].农业机械学报,2019,50(10):17-23+34.
[17] 薛雷, 曾宏伟, 覃程锦, 陶建峰, 刘成良等. 采用同步压缩变换和能量熵的机器人加工颤振监测方法[J].西安交通大学学报,2019,53(08):24-30+89.
[16] 张康, 陶建峰, 覃程锦, 李卫星, 刘成良. 随机丢弃和批标准化的深度卷积神经网络柴油机失火故障诊断[J].西安交通大学学报,2019,53(08):159-166.
[15] 齐文超, 李彦明, 陶建峰, 覃程锦, 刘成良, 种昆. 丘陵山地拖拉机姿态主动调整系统设计与实验[J].农业机械学报,2019,50(07):381-388.
[14] 蔡道清, 李彦明, 覃程锦, 刘成良. 水田田埂边界支持向量机检测方法[J].农业机械学报,2019,50(06):22-27+109.
[13] 陈建国, 李彦明, 覃程锦, 刘成良. 小麦精量播种机排种高精度检测系统设计与试验[J].农业机械学报,2019,50(01):66-74.
[12] 李卫星, 陶建峰, 覃程锦, 刘成良. 同步压缩小波与极限梯度提升树融合的柴油机失火故障诊断[J].西安交通大学学报,2019,53(02):47-54+169.
[11] 张伟, 杨刚, 雷军波, 刘成良, 陶建峰, 覃程锦. 基于微波反射法的谷物含水率在线检测装置研制[J].农业工程学报,2019,35(23):21-28.

Before 2019
[10] Qin C., Tao J., Liu C., A predictor-corrector-based holistic-discretization method for accurate and efficient milling stability analysis. International Journal of Advanced Manufacturing Technology, 2018, 96(5–8):2043–2054. (SCI, IF:3.563)
[09] Qin C., Tao J., Liu C., Stability analysis for milling operations using an Adams-Simpson-based method. International Journal of Advanced Manufacturing Technology, 2017, 92 (1–4):969–979. (SCI, IF:3.563)
[08] Qin C., Tao J., Liu C., An Adams-Moulton-based method for stability prediction of milling processes. International Journal of Advanced Manufacturing Technology, 2017, 89 (9–12):3049–3058. (SCI, IF:3.563)
[07] Qin C.*, Tao J., Wang M., Liu C., A novel approach for the acquisition of vibration signals of the end effector in robotic drilling. 2016 IEEE/CSAA International Conference on Aircraft Utility Systems, 2016, 7748106:522–526.
[06] Tao J., Qin C., Liu C.. Milling Stability Prediction with Multiple Delays via the Extended Adams-Moulton-Based Method. Mathematical Problems in Engineering, 2017, 2017:7898369. (SCI)
[05] Li, Y., Chen, J., Qin C., Liu, C.,Design of Precise Detection System for Wheat Seeding Quantity Based on AD7746 Analog to Digital Conversion Chip. Proceedings of 2018 International Computers, Signals and Systems Conference, 2018, pp. 706–711, 8941861.
[04] 陈建国, 李彦明, 覃程锦, 刘成良. 小麦播种量电容法检测系统设计与试验[J].农业工程学报,2018,34(18):51-58.
[03] 陶建峰, 朱瑶宏, 覃程锦, 庄欠伟. 类矩形隧道单机械臂管片拼装机运动学逆解[J].上海交通大学学报,2016,50(09):1473-1479.
[02] 王明斗, 陶建峰, 覃程锦, 刘成良. 空间余量最优的拼装机轨迹规划[J].浙江大学学报(工学版),2017,51(03):453-460.
[01] 陶建峰, 覃程锦, 黄德中, 顾建江. 类矩形盾构单机械臂管片拼装机静态误差计算[J].现代隧道技术,2016,53(S1):60-68.

教学工作：
2022春，课程名称：测试原理、传感与系统，授课对象：研究生，学时数：48；

本科班主任：
工科试验班F2102107班（分流前）
工科试验班F2102012班（分流后）

指导（或协助指导）学生
2018-曾宏伟，荣誉：研究生优秀奖学金、SMC奖学金、三好学生，毕业去向：华为技术有限公司武汉研究所
2019-刘明阳，荣誉：研究生优秀奖学金，毕业去向：华为云计算有限公司
2019-石岗，荣誉：研究生国家奖学金，毕业去向：上海交通大学机械与动力工程学院读博
2021-黄国强，硕士在读
2021-武睿宏，硕士在读
2022-石岗，博士拟录取

本科毕设：
2021-2022：换刀机器人遥操作系统设计与实现，文泓珺，吴佳坤，刘浩

本科全员导师：
19级，张奕鹏，易子琛，任宇，谢舒心

[01] 机械臂末端空间重复定位精度测量装置与方法. 中国，ZL201810322701.5
[02] 工业机器人末端重复定位精度测量装置与方法. 中国，ZL201810321280.4
[03] 自动定位夹紧装置及钢拱架的装配方法. 中国，ZL201811392006.2
[04] 钢拱架拼装中自动拧螺栓装置及方法. 中国，ZL201811341733.6
[05] 机械臂末端重复定位精度测量装置与方法. 中国，ZL201810322694.9
[06] 一种挖掘机液压缸泄漏检测方法和装置. 中国，ZL201711153639.3
[07] 一种基于粒子群优化算法PSO的TBM减振控制方法. 中国，ZL201711153618.1
[08] 油缸行程测量装置及其测量方法. 中国，ZL201711153636.X
[09] 强夯机夯深测量装置及测量方法. 中国，ZL2013101659280
[10] 一种油门控制装置. 中国，ZL201711204840.X
[11] 一种基于现场参数的硬岩TBM刀盘滚刀磨损实时评估方法. 中国，ZL202011023864.7
[12] 半监督的盾构隧道掌子面地质类型预估方法及系统. 中国，ZL202110552727.0
[13] 一种基于基因表达式编程的硬岩TBM滚刀磨损实时评估方法. 中国，ZL202011023865.1
[14] 一种基于PICA-VMD和Hilbert边际谱的轴向柱塞泵空化等级识别方法. 中国，CN110991544A
[15] 一种柱塞泵空化程度检测方法、装置及终端. 中国，CN111401136A
[16] 基于整体离散策略的铣削稳定性分析方法. 中国，CN110162733A
[17] 基于并联式神经网络的盾构机刀盘扭矩预测方法及系统. 中国，202011225143.4
[18] 一种盾构机刀盘扭矩实时预测方法及系统. 中国，202011220548.9
[19] 工业机器人钻孔颤振监测软件(颤振监测软件). 中国，2018R11L1128007
[20] 工业机器人智能健康管理平台. 中国，2021SR0101081
[21] 拖拉机故障诊断与智能运维系统软件. 中国，2021SR0367168
[22] 一种基于盾构机运行参数的掘进掌子面岩土类型识别方法. 中国，CN202110407464.4
[23] 盾构刀盘扭矩多步预测方法和系统. 中国，CN202110534801.6
[24] 一种基于深度学习的机器人避障轨迹规划方法及系统. 中国，CN109213147.A
[25] 一种基于混合深度神经网络的谐波减速器功率实时预测方法. 中国，CN202110672801.2
[26] 一种基于SVR与PSO的盾构利用率预测与操作参数优化方法及系统. 中国，CN202110775455.0
[27] 一种盾构刀盘扭矩领域自适应迁移预测方法和系统. 中国，CN202110775453.1
[28] 一种TBM利用率预测的A-CNN方法. 中国，CN202110976876.X
[29] 机器人钻削颤振识别方法和系统. 中国，CN202111221868.0
[30] 一种基于深度卷积残差收缩网络的刀具磨损预测方法和系统. 中国，CN202111216259.6
[31] 盾构装备健康监控与智能运维平台. 中国，2021SR2088477
[32] 地下工程装备状态监测与地质感知系统. 中国，2022SR0191354
[33] 隧道掘进机操作参数地层自适应决策方法及系统. 中国，CN202210134013.2
[34] 全断面隧道掘进机刀盘扭矩长时间预测方法及系统. 中国，CN202210129526.4
[35] 基于深度残差网络的盾构机刀盘结泥饼诊断方法和系统. 中国，CN202210396985.9
[36] 基于历史运行数据的盾构机刀盘结泥饼诊断方法和系统. 中国，CN202210395715.6
[37] 柴油机失火抗噪和跨噪声域诊断方法及系统. 中国，CN202210589677.8
[38] 盾构机施工掌子面地质类型识别方法及系统. 中国，CN202210594109.7
[39] 超高速电梯滚动导靴磨损性能测试装置及测试方法. 中国，提交
[40] 换刀机器人遥操作平台V1.0. 中国，提交

Editorial board：
[01] 2022.02至今, International Journal of Hydromechatronics, Youth Editorial board member 青年编委
[02] 2022.04至今, Journal of Dynamics, Monitoring and Diagnostics, Youth Editorial board member 青年编委

Guest Editors：
[01] 2021.08-2022.08, 主题专刊 "Machine Learning in Vibration and Acoustics", Leading guest editor 客座主编, https://www.mdpi.com/journal/applsci/special_issues/Machine_Learning_Vibration_Acoustics
投稿截止日期：2022.08.31。涉及振动和声学的信号处理和机器学习以及PHM均符合主题，欢迎积极赐稿!

[02] 2022.02至今, 主题专刊 "Machine Learning in Data Processing of Wireless Sensor Networks", Guest editor, https://journals.sagepub.com/page/dsn/collections/special-issues/machine-learning-in-data-processing-of-wireless-sensor-networks

Reviewers：
担任 IEEE Transactions on Neural Networks and Learning Systems、IEEE Transactions on Industrial Informatics、IEEE Transactions on Industrial Electronics 、Mechanical Systems and Signal Processing、Applied Energy、IEEE Journal of Biomedical and Health Informatics、Journal of Intelligent Manufacturing、Nonlinear Dynamics等近30个国际期刊审稿人

[01] 2021.08 至今 IEEE Transactions on Neural Networks and Learning Systems 审稿人
[02] 2022.04 至今 IEEE Transactions on Industrial Informatics审稿人
[03] 2020.03 至今 IEEE Transactions on Industrial Electronics 审稿人
[04] 2021.08 至今 Mechanical Systems and Signal Processing 审稿人
[05] 2021.09 至今 IEEE Journal of Biomedical and Health Informatics 审稿人
[06] 2019.11 至今 Journal of Intelligent Manufacturing 审稿人
[07] 2016.09 至今 Nonlinear Dynamics 审稿人
[08] 2021.05 至今 Computers and Electronics in Agriculture 审稿人
[09] 2021.12 至今 ISA Transactions审稿人
[10] 2020.04 至今 Measurement 审稿人
[11] 2021.07 至今 Expert Systems With Applications 审稿人
[12] 2021.08 至今 Applied Acoustics 审稿人
[13] 2022.01 至今 Friction审稿人
[14] 2020.04 至今 CMES-Computer Modeling in Engineering & Sciences 审稿人
[15] 2020.04 至今 Complexity 审稿人
[16] 2019.11 至今 Artificial Intelligence in Agriculture 审稿人
[17] 2019.11 至今 IEEE Access 审稿人
[18] 2019.10 至今 Shock and Vibration 审稿人
[19] 2018.10 至今 International Journal of Advanced Manufacturing Technology 审稿人
[20] 2021.03 至今 Scientific Reports 审稿人
[21] 2019.04 至今 担任IEEE International Conference on PHM 等多个国际会议TPC和审稿人
[22] 2021.06 至今 Geomechanics and Engineering 审稿人
[23] 2021.09 至今 Intelligent Systems with Applications 审稿人
[24] 2021.10 至今 Energies审稿人
[25] 2021.11 至今 Sensors审稿人
[26] 2022.01 至今 Knowledge Engineering Review审稿人
[27] 2022.01 至今 Electronics审稿人
[28] 2022.05 至今 Applied Energy审稿人
[29] 2022.06 至今 Artificial Intelligence Review审稿人
[30] 2022.08 至今 Mechanics Based Design of Structures and Machines审稿人

[1] 中国机械工业科学技术特等奖，2020，中国机械工业联合会
[2] 神农中华农业科技进步一等奖，2021，中国农学会
[3] 吴文俊人工智能科技进步二等奖，2020，中国人工智能学会
[4] 上海市“超级博士后”激励计划，2019，上海人力资源和社会保障局
[5] 求学期间获国家/国家励志/研究生优秀/SMC特等奖学金和优秀毕业生等