毛永芳，重庆大学, 自动化学院, 副教授

教育背景：

(1)2004-09至2008-12, 重庆大学, 机械电子工程, 博士

(2)2000-09至2004-07, 重庆大学, 机械电子工程, 学士

(3)2013-01 至 2014-01, University of Michigan, IOE,访问学者

专业领域：

智能故障诊断技术、装备智能运维、产线数智化与质量控制

主要研究方向：

半监督/无监督学习方法、智能故障预测及诊断、运行安全评估及控制

主讲课程:

模式识别与机器学习、运行安全智能监测与控制

学术兼职和荣誉：

现任多部国际学术期刊审稿人，包括：

·IEEE Transactions on Industrial Informatics

·IEEE Transactions on Instrumentation & Measurement

·Mechanical System and Signal Processing

·Reliability Engineering & System Safety

·Advanced Engineering Informatics 

·Applied Soft Computing Journal

科研情况简介：

在机理与数据融合建模、多模态表征与融合、跨域迁移诊断、生成式深度神经网络等方面进行了长期研究，相关成果形成了专著一本，发表论文50篇，其中SCI检索33篇，高被引论文3篇，被引用50余次，EI检索15篇；研究成果主要发表在《IEEE Transactions on IndustrialInformatics》、《Engineering Applications of Artificial Intelligence 》、《Advanced Engineering Informatics》、《Mechanical Systems and Signal processing》、《IEEE Transactions on Instrumentation and Measurement》、《Knowledge-Based Systems》、《Journal of Sound and Vibration》《振动与冲击》、《机械工程学报》等国内外著名刊物上。2019年获仪器仪表学会科学技术一等奖1项、中国产学研合作创新成果一等奖1项，2025年获重庆市科技进步一等奖一项，并申请和授权了多项国家发明专利。

项目情况

(1)国家自然科学基金委员会,面上项目，物理约束下液氢加注系统多模态融合表征模型及迁移诊断方法，主持

(2)国家自然科学基金委员会,联合基金项目,高速铁路多专业协同安全控制模式与策略研究,参与

(3)国家自然科学基金委员会, 联合基金项目,基于大数据的航天发射系统安全性实时评估方法,参与

(4)科技部重点项目（课题），压力和流量标定智能管控技术研究,参与

(5)科技部重点项目（课题），钢铁生产流程物质-能量-成本-信息-控制五流耦合机理网络化模型研究,参与

(6)重庆市科学技术局, 技术创新与应用示范专项,电梯驱动主机故障深度可信诊断关键技术及检测仪器研发, 结题, 参与

论文及专著

（一）专著

[1]柴毅; 张可; 毛永芳; 魏善碧 ; 动态系统运行安全性分析与技术, 化学工业出版社, 2019

（二）论文（选录）

[1]Yi Qin,YihangZhao,Junyu Qi,Yongfang Mao. Spatial-temporal multi-sensor information fusion network with prior knowledge embedding for equipment remaining useful life prediction[J]. Reliability Engineering & System Safety, 2025: 111420.

[2]Yi Qin, Yuhang Jun,DingliangChen,Yongfang Mao. A prognostic driven dynamic predictive maintenance decision-making model for offshore wind turbine systems[J]. Ocean Engineering, 2025, 338: 122041.

[3]Yi Qin, LijuanZhao, Yuejian Chen,Dengyu Xiao,Yongfang Mao. Learnable wavelet-driven physically interpretable networks for bearing fault diagnosis under variable speed[J]. Mechanical Systems and Signal Processing, 2025, 237: 113121.

[4]Yi Qin, XiwenLiu,XinLi ,Yongfang Mao. Simulation-data Driven Generalized Zero-Shot Learning for Multi-agent Bearing Compound Fault Diagnosis[J]. Knowledge-Based Systems, 2025: 113595.

[5]KaixiongXu, ShuangLi, ShuiqingXu,Youqiang Hu,Yongfang Mao,Yi Chai. Mutual Information-guided Domain Shared Feature Learning for Bearing Fault Diagnosis under Unknown Conditions[J]. IEEE Transactions on Instrumentation and Measurement, 2025.

[6]Yi Qin,Hongyu Liu,YiWang,Yongfang Mao. Inverse physics–informed neural networks for digital twin–based bearing fault diagnosis under imbalanced samples[J]. Knowledge-Based Systems, 2024, 292: 111641.（高被引论文）

[7]Yi Qin,Hongyu Liu,Yongfang Mao. Faulty rolling bearing digital twin model and its application in fault diagnosis with imbalanced samples[J]. Advanced Engineering Informatics, 2024, 61: 102513.（高被引论文）

[8]huoLi,XiaolongChen,Yi Chai,Ke Zhang, Yongfang Mao. Mechanism-Assisted Deep State Space Model for Dynamic System Identification[J]. IEEE Transactions on Industrial Informatics, 2024.

[9]Dingliang Chen,Yi Chai,Yongfang Mao,Yi Qin. Unsupervised health indicator construction by a new Gaussian-student’s t-distribution mixture model and its application[J]. Advanced Engineering Informatics, 2024, 62: 102863.

[10]Yi Qin,RuiYang,Biao He,Dingliang Chen, Yongfang Mao. Multi-layer convolutional dictionary learning network for signal denoising and its application to explainable rolling bearing fault diagnosis[J]. ISA transactions, 2024, 147: 55-70.

[11]Yi Qin,Lv Wang,Quan Qian,Yongfang Mao. Zero-shot attribute consistent model for bearing fault diagnosis under unknown domain[J]. IEEE Transactions on Instrumentation and Measurement, 2024, 73: 1-11.

[12]Yi Qin,TaishengZhang,Quan Qian,Yongfang Mao. Large model for rotating machine fault diagnosis based on a dense connection network with depthwise separable convolution[J]. IEEE Transactions on Instrumentation and Measurement, 2024.

[13]Yi Qin,Xiwen Liu,Yongfang Mao. Multi-label decoupling diagnosis for compound faults by improved deep Q-network[J]. IEEE Transactions on Instrumentation and Measurement, 2024.

[14]Yi Qin,JiahongYang,JianghongZhou,Huayan Pu, Xiangfeng Zhang, Yongfang Mao. Dynamic weighted federated remaining useful life prediction approach for rotating machinery[J]. Mechanical Systems and Signal Processing, 2023, 202: 110688.

[15]Yi Qin,Quan Qian,ZhengyiWang,Yongfang Mao. Adaptive manifold partial domain adaptation for fault transfer diagnosis of rotating machinery[J]. Engineering Applications of Artificial Intelligence, 2023, 126: 107082.

[16]Yi Qin,Jiahong Yang,Jianghong Zhou,Huayan Pu, Yongfang Mao. A new supervised multi-head self-attention autoencoder for health indicator construction and similarity-based machinery RUL prediction[J]. Advanced engineering informatics, 2023, 56: 101973.

[17]Yi Qin,RuiYang,HaiyangShi,Biao He,Yongfang Mao. Adaptive fast chirplet transform and its application into rolling bearing fault diagnosis under time-varying speed condition[J]. IEEE Transactions on Instrumentation and Measurement, 2023, 72: 1-12.

[18]YuhuLiu,Xiaolong Chen,Yongfang Mao,Yi Chai,Yutao Jiang. Fault diagnosis of sensor pulse signals based on improved energy fluctuation index and VMD[J]. Frontiers in Physics, 2023, 11: 1124485.

[19]Yi Qin,QunwangYao,YiWang,Yongfang Mao,Yaguo Lei. Parameter sharing adversarial domain adaptation networks for fault transfer diagnosis of planetary gearboxes[J]. Mechanical Systems and Signal Processing, 2021, 160: 107936.

[20]CongmeiJiang,Yongfang Mao,Yi Chai,Mingbiao Yu. Day‐ahead renewable scenario forecasts based on generative adversarial networks[J]. International Journal of Energy Research, 2021, 45(5): 7572-7587.

联系方式：

yfm@cqu.edu.cn

重庆大学自动化学院，重庆大学虎溪校区信息楼A308