秦毅，男，1982年生，四川宜賓人，博士，重慶大學機械工程學院和機械傳動國家重點實驗室教授，博士生導師，機械電子工程系支部書記。主要從事機械狀態監測與故障診斷、智慧型製造、大數據處理與人工智慧、智慧型結構及其套用等領域的研究。現作為項目負責人承擔了國家自然科學基金面上研究項目、陸航預研項目、國家重點研發計畫子課題、機械傳動國家重點實驗室特色研究項目、重慶市基礎科學與前沿技術研究項目、中央高校基本科研業務費前沿交叉項目等多項國家和省部級項目。在國內外著名雜誌和會議上發表論文70餘篇，其中，SCI檢索30餘篇（被引用700餘次，其中他引600餘次），EI檢索26篇，ISTP檢索5篇；參編高等教育出版社出版教材1部。
基本介紹
- 中文名：秦毅
- 出生地：四川宜賓
- 出生日期：1982年8月
- 畢業院校：重慶大學
- 學位/學歷：博士
- 專業方向：機械狀態監測與故障診斷、智慧型製造、大數據處理與人工智慧等
- 職務：重慶大學機械電子工程系支部書記
- 主要成就：教育部科技進步一等獎
研究方向,人物經歷,獲獎記錄,兼任職務,學術成果,科研項目,主要專利,發表論文,
研究方向
機械狀態監測與故障診斷、智慧型製造、大數據處理與人工智慧、智慧型結構。
人物經歷
2000年至2004年在重慶大學機械工程及自動化專業學習，獲學士學位，並推免到重慶大學機械電子工程系攻讀碩士學位；
2004年至2008在重慶大學機械電子工程專業碩博連讀，獲工學博士學位，其博士論文獲重慶市優秀博士論文；
2013年1月至2014年1月在密西根大學安娜堡校區作訪問學者；
2012年1月至2017年3月在四川大學從事博士後研究工作；
2009年1月至今在重慶大學從事教學科研工作。
獲獎記錄
獲授權國家發明專利7項，實用新型1項；獲教育部科技進步一等獎2項、“重慶產學研創新成果獎”一等獎1項；獲計算機軟體著作權2件；榮獲科學中國人(2018)年度人物。
兼任職務
擔任了三十餘種期刊的的常任審稿人，並多次榮獲傑出審稿人稱號。IEEE Member、SPIE Member、中國機械工程學會高級會員、中國振動工程學會高級會員、中國力學學會高級會員、IEEE可靠性學會重慶分會副主席、中國振動工程學會故障診斷專業委員會理事、中國振動工程學會轉子動力學專業委員會理事、全國高校機械工程測試技術研究會理事和西南分會秘書長。
學術成果
科研項目
面向動軸齒輪傳動故障診斷的振動模型驅動稀疏表征理論研究 國家自然科學基金面上項目
齒輪傳動基礎數據與可靠性分析 機械傳動國家重點實驗室特色研究項目
基於耦合壓電阻抗的轉子複合損傷定量診斷方法研究 重慶市基礎科學與前沿技術研究項目
陸航十三五預研項目
數據驅動航空發動機主軸軸承故障預測研究重慶大學中央高校基本科研業務費前沿交叉研究專項項目
主要專利
多方向寬頻帶壓電振動發電裝置秦毅, 郭磊, 趙月, 湯寶平 發明 2018.5.22ZL201610442193.5
一種疊代Teager能量運算元解調方法與系統 秦毅, 毛永芳, 任兵, 周廣武 發明 2011.12.21 ZL201110430480.1
高性能機械基礎件精密成形智慧型製造系統 王家序, 秦毅, 韓彥峰, 崔洪斌 發明2 011.12.16 ZL201110420431.x
滾動軸承摩擦力矩試驗台 王家序，秦毅，趙慧，蒲偉, 李俊陽 發明 2012.04.16ZL201210108207.1
水潤滑軸承及傳動系統綜合性能實驗平台 王家序, 周廣武, 秦毅, 王戰江, 韓彥峰, 李敏, 李俊陽, 肖科 發明 2011.05.10 ZL2011101197668
發表論文
[1]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.
[2]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.
[3]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, 2019, DOI 10.1109/TII.2019.2909305
[4] 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.
[5]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.
[6]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.
[7]Yi Qin, Yongfang Mao, Baoping Tang, Yi Wang, Haizhou Chen. M-band flexible wavelet transform and its application into planetary gear transmission fault diagnosis.Mechanical Systems and Signal Processing, 2019
[8]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.
[9]Yi Qin, Baoping Tang, Jiaxu Wang. Higher density dyadic wavelet transform and its application. Mechanical Systems and Signal Processing, 2010, 24(3): 823-834.
[10]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.
[11]Yi Qin. Multicomponent AM–FM demodulation based on energyseparation and adaptive filtering. Mechanical Systems and Signal Processing, 2013, 38(2): 440-459.
[12]Yi Qin, YongfangMao, BaopingTang.Vibration signal component separation by iteratively using basis pursuit and its application in mechanical fault detection. JournalofSoundandVibration, 2013, 332(20): 5217-5235.
[13]Yi Qin, JiaxuWang,Yongfang Mao.Dense framelets with two generators and their application in mechanical fault diagnosis. Mechanical Systems and Signal Processing, 2013,40(2): 483-498.
[14]Yi Qin, Yongfang Mao, Baoping Tang. Multicomponent decomposition by wavelet modulus maximaand synchronous detection. Mechanical Systems and Signal Processing, 2017, 91: 57-80.
[15]Yi Qin, Yi Tao, Ye He, Baoping Tang. Adaptive bistable stochastic resonance and its application in mechanical fault feature extraction. JournalofSoundandVibration, 2014,333(26): 7386-7400.
[16]Yi Qin, Yi Tao, Yongfang Mao, Baoping Tang. Quantitative rotor damage detection basedon piezoelectric impedance . Measurement Science and Technology, 2015, 26: 125012.
[17]Yi Qin, Baoping Tang, Yongfang Mao. Adaptive signal decomposition based on wavelet ridge and its application. Signal Processing, 2016, 120: 480-494.
[18]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.
[19]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.
[20]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.
[21]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.
[22]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.
[23]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.
[24]Zuqiang Su, Baoping Tang, Ziran Liu,Yi Qin.Multi-fault diagnosis for rotating machinery based on orthogonal supervised linear local tangent space alignment and least square support vector machine. Neurocomputing, 2015, 157: 208-222
[25]Yongfang Mao, Shuren Qin,Yi Qin.Demodulation based on harmonic wavelet and its application into rotary machinery fault diagnosis. Chinese Journal of Mechanical Engineering, 2009, 22(3): 419-425.
[26]Baoping Tang, Feng Li,Yi Qin. Fault diagnosis model based on feature compression with orthogonal locality preserving projection. Chinese Journal of Mechanical Engineering, 2011, 24(5): 891-898.
