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秦毅

学院概况

姓名:秦毅

出生年月:1982/8/8

学历学位:博士

专业技术职务:教授/博士生导师

邮箱:qy_808@cqu.edu.cn

联系电话:13527479813

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个人简历

秦毅,男,四川宜宾人,1982年生,博士,mandetx手机登录和机械传动国家重点实验室教授,博士生导师,机械电子工程系支部书记,首批重庆英才·青年拔尖人才,重庆大学科研后备拔尖人才。2000年至2004年在重庆大学机械工程及自动化专业学习,获学士学位,并推免到重庆大学机械电子工程系攻读硕士学位;2004年至2008在重庆大学机械电子工程专业硕博连读,获工学博士学位,其博士论文获重庆市优秀博士论文;2009年1月留校任教;2013年1月至2014年1月在密西根大学安娜堡校区作访问学者;2012年1月至2017年3月在四川大学从事博士后研究工作。主要从事智能故障诊断与预测、大数据处理与人工智能、智能制造、动力学建模与数字孪生、机械测试与智能结构等领域的研究。现作为项目负责人承担了国家自然科学基金面上研究项目、陆航预研项目、国家重点研发计划子课题、两机专项子课题、重庆市基础科学与前沿技术研究项目等多项国家和省部级项目。在《IEEE Transactions on Industrial Electronics》、《IEEE Transactions on Industrial Informatics》、《Mechanical Systems and Signal Processing》、《Journal of Sound and Vibration》、《Signal Processing》、《IEEE Transactions on Instrumentation and Measurement》、《ISA Transactions》、《Renewable Energy》、《Engineering Applications of Artificial Intelligence》、《Measurement》等国内外著名杂志和会议上发表论文80余篇,其中,SCI检索47篇(被引用1150余次,其中他引900余次),EI检索26篇,ISTP检索5篇;参编高等教育出版社出版教材1部,获授权国家发明专利10项,实用新型2项;获计算机软件著作权2件;获重庆市科技进步一等奖1项、教育部科技进步一等奖2项与重庆产学研创新成果奖一等奖1项;荣获英国物理学会(IOP)出版社2019高被引作者奖、IEEE Reliability Society Chongqing Chapter杰出青年科学家、科学中国人(2018)年度人物。担任了长江学者计划、国家自然科学基金和河北省科技奖等通讯评审专家,《Shock and Vibration》和《Mechanical Engineering Science》编委、仪器仪表学报客座副主编以及《IEEE Transactions on Industrial Electronics》、《IEEE Transactions on Industrial Informatics》、IEEE Transactions on Signal Processing》、《IEEE Transactions on Power Electronics》、《Mechanical Systems and Signal Processing》、《IEEE Transactions on Instrumentation and Measurement》、《Journal of Sound and Vibration》、《Signal Processing》、《Engineering Applications of Artificial Intelligence》、《Neurocomputing》、《IEEE Systems Journal》、《IEEE Access》、《Journal of Micromechanics and Microengineering》、《International Journal of Energy Research》、《Journal of Vibration and Control》、《Measurement Science and Technology》、《Applied Mathematical Modelling》、《Measurement》、《Circuits, Systems, and Signal Processing》、《Optics and Laser Technology》、《Fluctuation and Noise Letters》、《Advances in Mechanical Engineering》、《Shock and Vibration》、《Discrete Dynamics in Nature and Society》、《Optik - International Journal for Light and Electron Optics》、《Sensors》、《Engineering Science and Technology, an International Journal》、《Journal of the Brazilian Society of Mechanical Sciences and Engineering》、《Frontiers in Energy》、《Mathematical Problems in Engineering》、《International Journal of Modelling and Simulation》、《Measurement and Control》、《Advanced Composites Letters》、《仪器仪表学报》、《振动与冲击》、《华中科技大学学报》、《振动、测试与诊断》、《中国机械工程》等国内外期刊的常任审稿人,并多次荣获杰出审稿人称号;同时还是IEEE Senior Member、SPIE Member、中国机械工程学会高级会员、中国振动工程学会高级会员、中国力学学会高级会员、IEEE可靠性学会重庆分会副主席、中国振动工程学会故障诊断专业委员会理事、中国振动工程学会转子动力学专业委员会理事、全国高校机械工程测试技术研究会理事。

主要研究方向

智能故障诊断与预测、大数据处理与人工智能、智能制造、动力学建模与数字孪生、机械测试与智能结构

毕业研究生去向

高校、研究所、海康威视、宁德时代、华为、TP-LINK、上海发那科机器人、上海汽车等国内外著名企业

主要研究经历、荣誉称号、获奖情况、社会兼职等

2000年至2004年在重庆大学机械工程及自动化专业学习,获学士学位,并推免到重庆大学机械电子工程系攻读硕士学位;

2004年至2008在重庆大学机械电子工程专业硕博连读,获工学博士学位,其博士论文获重庆市优秀博士论文;

2013年1月至2014年1月在密西根大学安娜堡校区作访问学者;

2012年1月至2017年3月在四川大学从事博士后研究工作;

2009年1月至今在重庆大学从事教学科研工作。

主要从事机械状态监测与故障诊断、智能制造、大数据处理与人工智能、智能结构及其应用等领域的研究。现作为项目负责人承担了国家自然科学基金面上研究项目、陆航预研项目、国家重点研发计划子课题、机械传动国家重点实验室特色研究项目、重庆市基础科学与前沿技术研究项目、中央高校基本科研业务费重点项目等多项国家和省部级项目。

获授权国家发明专利10项,实用新型2项;获重庆市科技进步一等奖1项、教育部科技进步一等奖2项与重庆产学研创新成果奖一等奖1项;获计算机软件著作权2件;荣获英国物理学会(IOP)出版社2019高被引作者奖、IEEE Reliability Society Chongqing Chapter杰出青年科学家、科学中国人(2018)年度人物。

担任了三十余种期刊的的常任审稿人,并多次荣获杰出审稿人称号。

IEEE Senior Member、SPIE Member、中国机械工程学会高级会员、中国振动工程学会高级会员、中国力学学会高级会员、IEEE可靠性学会重庆分会副主席、中国振动工程学会故障诊断专业委员会理事、中国振动工程学会转子动力学专业委员会理事、全国高校机械工程测试技术研究会理事和西南分会秘书长。

 

主要在研科研项目:

高性能齿轮疲劳试验检测新技术   国家重点研发计划重点专项子课题

面向动轴齿轮传动故障诊断的振动模型驱动稀疏表征理论研究   国家自然科学基金面上项目

某传动系统关键技术研究项目子课题  两机专项

陆航十三五预研项目

直升机主减行星传动系统故障诊断与预测   重庆大学科研后备拔尖人才项目


主要发明专利:

[1] 秦毅,郭磊,赵月,汤宝平,多方向宽频带压电振动发电装置,2016.6.20,中国,ZL201610442193.5 公告日:2018.05.22

[2] 秦毅,袁鹏,邢剑锋,张清亮,基于耦合压电阻抗的转子损伤检测方法,2016.7.11,中国,ZL201610541260.9 公告日:2019.09.23

[3] 秦毅,韦甜甜,赵月,陈伟伟,桥式自适应压电能量采集器,2018.8.24,中国,ZL201810973298.2 公告日:2019.12.06

[4] 秦毅,毛永芳,任兵,周广武,一种迭代Teager能量算子解调方法与系统,2012.7.11-2031.12.20,中国,ZL201110430480.1. 公告日:2014.08.06

[5] 王家序,秦毅,韩彦峰,崔洪斌,高性能机械基础件精密成形智能制造系统,2012.6.13-2031.12.15,中国,ZL201110420431.X. 公告日:2013.09.04

[6] 王家序,秦毅,赵慧,蒲伟,李俊阳,滚动轴承摩擦力矩试验台,2012.7.25-2032.4.13,中国,ZL201210108207.1. 公告日:2013.10.23


个人学术主页:https://qinyi-team.github.io/#blog

公开发表论文(代表作)

[1] Yi Qin*, Sheng Xiang, Yi Chai, Haizhou Chen. Macroscopic-microscopic attention in LSTM networks based on fusion features for gear remaining life prediction. IEEE Transactions on Industrial Electronics, 2020, 67(12): 10865-10875.

[2] Yi Qin*, Dingliang Chen, Sheng Xiang, Caichao Zhu. Gated dual attention unit neural networks for remaining useful life prediction of rolling bearings. IEEE Transactions on Industrial Informatics, 2020, DOI 10.1109/TII.2020.2999442.

[3] Sheng Xiang, Yi Qin*, Caichao Zhu, Yangyang Wang, Haizhou Chen. Long short-term memory neural network with weight amplification and its application into gear remaining useful life prediction. Engineering Applications of Artificial Intelligence, 2020, 91: 103587.

[4] Yi Qin*. A new family of model-based impulsive wavelets and their sparse representation for rolling bearing fault diagnosis. IEEE Transactions on Industrial Electronics, 2018, 65(3): 2716-2726.

[5] Yi Qin*, Xin Wang, Jingqiang Zou. The optimized deep belief networks with improved logistic Sigmoid units and their application in fault diagnosis for planetary gearboxes of wind turbines. IEEE Transactions on Industrial Electronics, 2019, 66(5): 3814-3824.

[6] Yi Qin*, Jingqiang Zou, Baoping Tang, Yi Wang, Haizhou Chen. Transient feature extraction by the improved orthogonal matching pursuit and K-SVD algorithm with adaptive transient dictionary, IEEE Transactions on Industrial Informatics, 2020, 16(1): 215-227.

[8] Yi Qin*, Chengcheng Li, Xingguo Wu, Yangyang Wang, Haizhou Chen. Multiple-degree-of-freedom dynamic model of rolling bearing with a localized surface defect. Mechanism and Machine Theory, 2020, 154: 104047.

[9] Sheng Xiang, Yi Qin*, Caichao, Zhu, Yangyang Wang, Haizhou Chen. LSTM networks based on attention ordered neurons for gear remaining life prediction. ISA Transactions, 2020, https://doi.org/10.1016/j.isatra.2020.06.023.

[10] Yi Qin*, Yongfang Mao, Baoping Tang, Yi Wang, Haizhou Chen. M-band flexible wavelet transform and its application to the fault diagnosis of planetary gear transmission systems. Mechanical Systems and Signal Processing, 2019, 134: 106298.

[11] Siliang Lu, Yi Qin*, Jun Hang, Baohua Zhang, Qunjing Wang. Adaptively Estimating Rotation Speed from DC Motor Current Ripple for Order Tracking and Fault Diagnosis. IEEE Transactions on Instrumentation and Measurement, 2019, 68(3): 741-753.

[12] Haoran Yan, Yi Qin*, Sheng Xiang, Yi Wang, Haizhou Chen. Long-term gear life prediction based on ordered neurons LSTM neural networks. Measurement, 2020, 165: 108205.

[13] Chengcheng Li, Yi Qin*, Yi Wang, Haizhou Chen. Vibration analysis of deep groove ball bearings with local defect using a new displacement excitation function. Journal of Tribology, 2021, https://doi.org/10.1115/1.4048163

[14] Xin Wang, Yi Qin*, Yi Wang, Sheng Xiang, Haizhou Chen. ReLTanh: An activation function with vanishing gradient resistance for SAE-based DNNs and its application to rotating machinery fault diagnosis, Neurocomputing, 2019, 363: 88-98.

[15] Yi Qin*, Chengcheng Li, Folin Cao, Haizhou Chen. A fault dynamic model of high-speed angular contact ball bearings. Mechanism and Machine Theory, 2020, 143: 103627.

[16] Yi Qin*, Tiantian Wei, Yue Zhao, Haizhou Chen. Simulation and experiment on bridge-shaped nonlinear piezoelectric vibration energy harvester. Smart Materials and Structures, 2019, 28: 045015.

[17] Yi Qin*, Folin Cao, Yi Wang, Weiwei Chen, Haizhou Chen. Dynamics modelling for deep groove ball bearings with local faults based on coupled and segmented displacement excitation. Journal of Sound and Vibration, 2019, 447: 1-19.

[18] Yi Qin*, Shuren Qin, Yongfang Mao. Research on iterated Hilbert transform and its application in mechanical fault diagnosis. Mechanical Systems and Signal Processing, 2008, 22(8): 1967-1980.

[19] Yi Qin*, Baoping Tang, Jiaxu Wang. Higher density dyadic wavelet transform and its application. Mechanical Systems and Signal Processing, 2010, 24(3): 823-834.

[20] Yi Qin*, Jiaxu Wang, Baoping Tang, Yongfang Mao. Higher density wavelet frames with symmetric low-pass and band-pass filters. Signal Processing, 2010, 90(12): 3219-3231.

[21] Yi Qin*. Multicomponent AM–FM demodulation based on energy separation and adaptive filtering. Mechanical Systems and Signal Processing, 2013, 38(2): 440-459.

[22] Yi Qin*, Yongfang Mao, Baoping Tang. Vibration signal component separation by iteratively using basis pursuit and its application in mechanical fault detection. Journal of Sound and Vibration, 2013, 332(20): 5217-5235.

[23] Yi Qin*, Jiaxu Wang, Yongfang Mao. Dense framelets with two generators and their application in mechanical fault diagnosis. Mechanical Systems and Signal Processing, 2013, 40(2): 483-498.

[24] Yi Qin*, Yongfang Mao, Baoping Tang. Multicomponent decomposition by wavelet modulus maxima and synchronous detection. Mechanical Systems and Signal Processing, 2017, 91: 57-80.

[25] Yi Qin*, Yi Tao, Ye He, Baoping Tang. Adaptive bistable stochastic resonance and its application in mechanical fault feature extraction. Journal of Sound and Vibration, 2014, 333(26): 7386-7400.

[26] Yi Qin*, Yi Tao, Yongfang Mao, Baoping Tang. Quantitative rotor damage detection based on piezoelectric impedance . Measurement Science and Technology, 2015, 26: 125012.

[27] Yi Qin*, Baoping Tang, Yongfang Mao. Adaptive signal decomposition based on wavelet ridge and its application. Signal Processing, 2016, 120: 480-494.

[28] Yi Qin*, Baoping Tang, Jiaxu Wang, Ke Xiao. A new method for multicomponent signal decomposition based on self-adaptive filtering. Measurement, 2011, 44(7): 1312-1327.

[29] Yi Qin*, Jianfeng Xing, and Yongfang Mao. Weak transient fault feature extraction based on an optimized Morlet wavelet and kurtosis. Measurement Science and Technology, 2016, 27: 085003.

[30] Yi Qin*, Qingliang Zhang, Yongfang Mao, Baoping Tang. Vibration component separation by iteratively using stochastic resonance with different frequency-scale ratios. Measurement, 2016, 94: 538-553.

[31] Yi Qin*, Shuren Qin, Yongfang Mao. Fast implementation of orthogonal empirical mode decomposition and its application into harmonic detection. Chinese Journal of Mechanical Engineering, 2008, 21(2): 93-98.

[32] Xin Wang, Yi Qin*, and Aibing Zhang. An intelligent fault diagnosis approach for planetary gearboxes based on deep belief networks and uniformed features. Journal of Intelligent & Fuzzy Systems, 2018, 3.

[33] Yonghua Jiang, Baoping Tang, Yi Qin, Wenyi Liu. Feature extraction method of wind turbine based on adaptive Monet wavelet and SVD. Renewable Energy, 2011, 36(8): 2146-2153.

 

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