2012.09-2018.12：上海交通大学机械电子工程专业，博士（硕博连读）
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
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）

1. 复杂机-电-液系统动力学建模、分析与控制
2. 工程机械装备(掘进机等)健康评估与智能运维
3. 工业机器人故障诊断、健康评估与智能运维
4. 信号处理、数据挖掘与人工智能理论与方法

欢迎对科研有热情的硕士和博士研究生联系学习和交流！

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
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
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（SAMC14-JS-15-046）”，参与

[1] 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. https://doi.org/10.1016/j.ymssp.2020.107386. (SCI, IF: 6.471)
[2] 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 Systems, 2020, 203:106122. (SCI, IF: 5.921)
[3] 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:3.108)
[4] 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:2.925)
[5] 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:2.925)
[6] 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:2.925)
[7] 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:2.925)
[8] Qin C., Tao J., Liu C., A novel stability prediction method for milling operations using the holistic-interpolation scheme. Journal of Mechanical Engineering Science, 2019, 233(13):4463–4475. (SCI, IF:1.386)
[9] 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:2.925)
[10] 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:4.311)
[11] 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. https://doi.org/10.1007/s10845-020-01577-y. (SCI, IF:4.311)
[12] 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. https://doi.org/10.1016/j.measurement.2020.108500. (SCI, IF: 3.364)
[13] 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: 5.669)
[14] 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: 2.215)
[15] 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:2.925)
[16] 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, accepted. (SCI, IF: 2.733)
[17] 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:2.925)
[18] 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: 3.275)
[19] 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: 3.137)
[20] 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, in press. (SCI, IF: 3.364)
[21] 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.
[22] Shi H., Qin C., Xiao D., Zhao L., Liu C., Automated heartbeat classification based on deep neural network with multiple input layers. Knowledge-Based Systems, 2020, 188:10503. (SCI, IF: 5.921)
[23] 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 Systems, 2020, 193:105460. (SCI, IF: 5.921)
[24] 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, 2020, in press. (SCI, IF:3.658)
[25] 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: 3.632)
[26] 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: 3.632)
[27] Xiao D., Huang Y., Zhao L., Qin C., Shi H., Liu C.. Unsupervised deep representation learning for motor fault diagnosis by mutual information maximization. IEEE Access, 2019, 7:80937-80949. (SCI, IF: 3.745)
[28] 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, IF: 1.009)
[29] 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, IF: 1.298)
[30] Ling X., Tao J., Li B., Qin C., Liu C.. A Multi-physics modeling-based vibration prediction method for switched reluctance motors. Applied Sciences (Switzerland), 2019, 9(21):4544. (SCI, IF: 2.474)
[31] 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, IF:1.386)
[32] 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.
[33] 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.

[1] 机械臂末端空间重复定位精度测量装置与方法. 中国，ZL201810322701.5
[2] 工业机器人末端重复定位精度测量装置与方法. 中国，ZL201810321280.4
[3] 自动定位夹紧装置及钢拱架的装配方法. 中国，ZL201811392006.2
[4] 钢拱架拼装中自动拧螺栓装置及方法. 中国，ZL201811341733.6
[5] 机械臂末端重复定位精度测量装置与方法. 中国，ZL201810322694.9
[6] 一种挖掘机液压缸泄漏检测方法和装置. 中国，ZL201711153639.3
[7] 一种基于粒子群优化算法PSO的TBM减振控制方法. 中国，ZL201711153618.1
[8] 油缸行程测量装置及其测量方法. 中国，ZL201711153636.X
[9] 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] 基于整体离散策略的铣削稳定性分析方法. 中国，CN110162733A
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[13] 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
[14] 一种柱塞泵空化程度检测方法、装置及终端. 中国，CN111401136A
[15] 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，202011023864.7
[16] 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，202011023865.1
[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

[1] 2020.03 至今 IEEE Transactions on Industrial Electronics 审稿人
[2] 2020.04 至今 Measurement 审稿人
[3] 2020.04 至今 CMES-Computer Modeling in Engineering & Sciences 审稿人
[4] 2020.04 至今 Complexity 审稿人
[5] 2019.11 至今 Journal of Intelligent Manufacturing 审稿人
[6] 2019.11 至今 IEEE Access 审稿人
[7] 2019.10 至今 Shock and Vibration 审稿人
[8] 2016.09 至今 Nonlinear Dynamics 审稿人

[1] 中国机械工业科学技术特等奖（排11），2020，中国机械工业联合会
[2] 吴文俊人工智能科技进步奖（排10），2020，中国人工智能学会
[3] 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