学术著作

[1] 丁国富，郑庆，张楷，刘贵杰，王淑营，张海柱，复杂装备全生命周期信息物理融合理论与方法, 科学出版社, 2023,12. ISBN 978-7-03-076118-7. 

第一/通讯作者 期刊论文

[24] Zhang K, Wang B, Zheng Q*, et al. A Novel Fault Diagnosis of High-Speed Train Axle Box Bearings with Adaptive Curriculum Self-Paced Learning under Noisy Labels. Structural Health Monitoring, Acceped 2025. (中科院2区/JCR Q1 IF 6.6，SCI：，EI：)

[23] Zheng Q, Teng P, Zhang K*, et al. A generalized network with domain invariance and specificity representation for bearing remaining useful life prediction under unknown conditions[J]. Knowledge-Based Systems. Available online 22 December 2024, 112915.  (领域Top期刊，中科院1区/JCR Q1，IF 7.2，SCI：，EI：）

[22]  Zheng Q, Wang D, Zhang K*, et al. A parametric study on the fatigue life of elevator brake wheels under multi-field coupling effects[J]. Engineering Failure Analysis. Available online 14 November 2024, 109061.  (中科院2区/JCR Q1，IF 4.4，SCI：001359626500001，EI:20244717386443）

[21] Huang F, Zhang K*, Zheng Q, et al. An Open-Set Method for Diagnosing Unknown Bogie Bearing Faults Using a Hybrid Open Score Relation Network[J]. IEEE Transactions on Instrumentation & Measurement. 2024.  (领域Top期刊，中科院2区/JCR Q1，IF 5.6，SCI：001346129100017，EI：20244417277258）

[20] Lai X, Zhang K*, Zheng Q, et al. DP2Net: A discontinuous physical property-constrained single-source domain generalization network for tool wear state recognition [J]. Mechanical Systems and Signal Processing, 2024.  (领域Top期刊，中科院1区/JCR Q1 IF 8.4，SCI：001230576800001，EI：20241615930723)

[19]  Huang F, Zhang K*, Li Z, et al. A rolling bearing fault diagnosis method based on interactive generative feature space oversampling-based autoencoder under imbalanced data[J]. Structural Health Monitoring, 2024: 147592172412482 09. (中科院2区/JCR Q1 IF 6.6，SCI：001220150800001，EI：20242016092766)

[18] Qin G, Zhang K*, Lai X, et al. An Adaptive Symmetric Loss in Dynamic Wide-Kernel ResNet for Rotating Machinery Fault Diagnosis Under Noisy Labels[J]. IEEE Transactions on Instrumentation and Measurement, 2024. (领域Top期刊，中科院2区/JCR Q1，IF 5.6，SCI：001197885000030，EI：20241215768343）

[17] 赖旭伟, 丁昆, 张楷*等. 基于可解释物理引导空间注意力改进的跨工艺参数立铣刀磨损辨识 [J/OL]. 机械工程学报, 1-11[2024-03-12]. http://kns.cnki.net/kcms/detail/11.2187.TH.20240118. 1103. 034.html.（机械工程领域高质量科技期刊分级T1，EI：20243516961760）

[16] Zuo T, Zhang K*, Zheng Q, et al. A hybrid attention-based multi-wavelet coefficient fusion method in RUL prognosis of rolling bearings [J]. Reliability Engineering & System Safety, 2023, 237: 109337.. (领域Top期刊， 中科院1区/JCR Q1 IF 8.1，SCI：001000667300001，EI：20232014092477)

[15] Zhang K, Tang B*, Deng L, et al. A fault diagnosis method for wind turbines gearbox based on adaptive loss weighted meta-ResNet under noisy labels [J]. Mechanical Systems and Signal Processing, 2021, 161: 107963. (领域Top期刊，中科院1区/JCR Q1 IF 8.4，SCI: 000670074900011，EI: 20211710255540)

[14] Zhang K, Tang B*, Deng L, et al. Fault source location of wind turbine based on heterogeneous nodes complex network [J]. Engineering Applications of Artificial Intelligence, 2021, 103: 104300. (领域Top期刊，中科院2区/JCR Q1，IF 8.0，SCI: 000709743500002, EI: 20212210425770)

[13] Zhang K, Tang B*, Deng L, et al. Fault Detection of Wind Turbines by Subspace Reconstruction-Based Robust Kernel Principal Component Analysis  [J]. IEEE Transactions on Instrumentation and Measurement, 2021, 70: 1-11. (领域Top期刊，中科院2区/JCR Q1，IF 5.6，SCI:000709736100001，EI: 20211810302812）

[12] Zhang K, Tang B*, Deng L, et al. A hybrid attention improved ResNet based fault diagnosis method of wind turbines gearbox [J]. Measurement, 2021, 179: 109491. (ESI高被引SCI论文，领域Top期刊，中科院2区/JCR Q1，IF 5.6，SCI: 000866496200004，EI: 20224212973672）

[11] Zhang K, Li Z, Zheng Q, et al. Fault Diagnosis With Bidirectional Guided Convolutional Neural Networks Under Noisy Labels  [J]. IEEE Sensors Journal, 2023. (中科院2区/JCR Q1，IF 4.3，SCI：001049997900095，EI：20232714336869)

[10] Zhang K, Tang B*, Qin Y, et al. Fault diagnosis of planetary gearbox using a novel semi-supervised method of multiple association layers networks [J]. Mechanical Systems and Signal Processing, 2019, 131: 243-260. (领域Top期刊，中科院1区/JCR Q1 IF 8.4，SCI:000487008600014, EI: 20192307001751)

[9] Zhang K, Ding K*, Zheng Q, et al. A novel cross-bearing fault diagnosis method based on pseudo-label transitive domain adaptation networks [J]. Journal of Vibration and Control, 2023: 10775463231202550. [J]. Journal of Vibration and Control, 2023: 10775463231202550. (中科院3区/JCR Q1，IF 2.8，SCI：001068209800001，EI：20233814763906）

[8] Zhang K*, Liu Y, Zou Y, et al. Degradation trend feature generation improved rotating machines RUL prognosis method with limited run-to-failure data [J]. Measurement Science and Technology, 2023, 34(7): 075019. (仪器仪表领域高质量科技期刊分级T1，中科院3区/JCR Q1，IF 2.4，SCI: 000970995100001 , EI: 20231714021102）

[7] Lai X, Zhang K*, Zheng Q, et al. A frequency-spatial hybrid attention mechanism improved tool wear state recognition method guided by structure and process parameters  [J]. Measurement, 2023, 214: 112833. (领域Top期刊，中科院2区/JCR Q1，IF 5.6，SCI: 000975644200001, EI:20231513863198）.

[6] Zou Y, Ding K, Shi K, Zhang K*, et al. Wear identification of end mills based on a feature-weighted convolutional neural network under unbalanced samples  [J]. Journal of Manufacturing Processes, 2023, 89: 64-76. (领域Top期刊，中科院2区/JCR Q1，IF 6.2，SCI: 000968232400001, EI: 20230713582179）

[5] Huang F, Li X, Zhang K*, et al. A novel simulation-assisted transfer method for bearing unknown fault diagnosis[J]. Measurement Science and Technology, 2024. (仪器仪表领域高质量科技期刊分级T1，中科院3区/JCR Q1，IF 2.4，SCI：001275378100001，EI：20243116770093）

[4] Wang L, Zhang K*, Zheng Q, et al. An undercarriage image driven anomaly detection method for metro vehicle based on adversarial memory enhancement [J]. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, 2023: 09544097231201519. (中科院4区/JCR Q2，IF 2，SCI：001066584700001，EI：20233814740127）

[3] 秦国浩, 张楷*, 丁昆等. 动态宽卷积残差网络的轴承故障诊断方法 [J]. 中国机械工程, 2023, 34 (18): 2212-2221. （机械工程领域高质量科技期刊分级T2，EI：20234515039342）

[2] Li Z, Zhang K*, Liu Y, et al. A Novel Remaining Useful Life Transfer Prediction Method of Rolling Bearings Based on Working Conditions Common Benchmark [J]. IEEE Transactions on Instrumentation and Measurement, 2022, 71: 1-9. (领域Top期刊，中科院2区/JCR Q1，IF 5.6， SCI:000709736100001，EI: 20211810302812）

[1] 张楷, 罗怡澜, 邹益胜等. 高速列车的样本关联改进故障诊断方法 [J]. 中国机械工程, 2018, 29 (02): 151-157. （机械工程领域高质量科技期刊分级T2，EI: 20184706123055）

第一/通讯作者 会议论文

 [5] Li Z, Zhang K*, Lai X, et al. A Novel Remaining Useful Life Prediction Method for Milling Tool Based on Transfer Hierarchical Vision Transformer [C]//2023 IEEE Smart World Congress (SWC). IEEE, 2023: 1-6.（EI：20241115733882）

 [4] Lai X, Zhang K*, Li Z, et al. Review of Interpretable Deep Learning in Fault Diagnosis and Its Application Prospectives in Condition Monitoring [C]//2023 IEEE Smart World Congress (SWC). IEEE, 2023: 1-6. （EI：20241115733672）

[3] Lai X, Zhang K*, Li Z, et al. Tool Wear State Identification Based on Frequency Domain Denoising and Frequencies-Separation Attention Networks [C]//2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS). IEEE, 2023: 1020-1024. （EI：20233114457306）

[2] Li Z, Zhang K*, Lai X, et al. A Remaining Useful Life Prediction Method for Rolling Bearing Based on Multi-channel Fusion Hierarchical Vision Transformer [C]//2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS). IEEE, 2023: 1025-1029. （EI：20233114457767）

[1] 秦国浩,黄锋飞,张楷*,等. 噪声标签下残差神经网络损失改进的轴承故障诊断方法[C]// 中国振动工程学会转子动力学专业委员会. 第15届全国转子动力学学术大会摘要集. 2023: 1. DOI:10.26914/c.cnkihy.2023.111648.

其他参与论文

[22] Jiang J, Tao G, Liang H, Zhang Kai, et al. A structure information-assisted generalization network for fault diagnosis of out-of-round wheels of metro trains[J]. Measurement, 2024: 116519.

[21] Liu M, Zhang J, Qin S,  Zhang Kai, et al. A multi-target regression-based method for multiple orders remaining completion time prediction in discrete manufacturing workshops[J]. Measurement, 2024: 116231.

[20] Fu H, Li Z, Xiao X, et al. Multi-Source Domain Generalization Tool Wear Prediction Based on Wide Convolution Weighted Antagonism[J]. Measurement Science and Technology, 2024.

[19] Hao W, Chen C, Huang F, et al. Fault Diagnosis Method for Rolling Bearings Based on CVAE-GAN Under Limited Data[C]//International conference on the Efficiency and Performance Engineering Network. Cham: Springer Nature Switzerland, 2024: 144-154.

[18] Yu X, Tang B, Zhang K. Fault diagnosis of wind turbine gearbox using a novel method of fast deep graph convolutional networks[J]. IEEE Transactions on Instrumentation and Measurement, 2021, 70: 1-14. (ESI高被引SCI论文，领域Top期刊，中科院2区/JCR Q1，IF 5.6）

[17] Huang H, Tang B, Luo J, et al. Residual gated dynamic sparse network for gearbox fault diagnosis using multisensor data[J]. IEEE Transactions on Industrial Informatics, 2021, 18(4): 2264-2273.

[16] Zhou W, Xiao X, Li Z, Zhang K, et al. Prediction tool wear using improved deep extreme learning machines based on the sparrow search algorithm[J]. Measurement Science and Technology, 2024, 35(4): 046112.

[15] 龙辉,陶功权,梁红琴,等. 地铁扁疤车轮通过钢轨焊缝区的轮轨垂向力特性分析 [J/OL]. 机械工程学报, 1-11[2024-04-30]. http://kns.cnki.net/kcms/detail/11.2187.TH.20240418.1523.036.html.

[14] Zheng Q, Ding G, Zhang H, Zhang K, et al. An application-oriented digital twin framework and the multi-model fusion mechanism[J]. International Journal of Computer Integrated Manufacturing, 2023: 1-24.

[13] Liu Y, Zou Y, Zhang K. Transfer Prediction Method of Bearing Remaining Useful Life Based on Deep Feature Evaluation under Different Working Conditions[J]. Sensors, 2023, 23(19): 8254.

[12] Hao W, Li Z, Qin G, et al. A novel prediction method based on bi-channel hierarchical vision transformer for rolling bearings’ remaining useful life[J]. Processes, 2023, 11(4): 1153.

[11]  梁红琴,姜进南,陶功权等. 基于轴箱垂向振动加速度的地铁车轮失圆状态诊断方法 [J]. 中南大学学报(自然科学版), 2024, 55 (01): 431-443.

[10]  郝伟,丁昆,暴长春,等. 小样本下多尺度卷积关系网络的轴承故障诊断方法 [J]. 中国测试, 2024, 50 (03): 160-168.

[9]  刘云飞,张楷,菅紫倩,等. 基于深度SVDD-CVAE的轴承自适应阈值故障检测 [J]. 机床与液压, 2024, 52 (06): 177-183+195.

[8] 余浩帅, 汤宝平, 张楷等. 小样本下混合自注意力原型网络的风电齿轮箱故障诊断方法 [J]. 中国机械工程, 2021, 32 (20): 2475-248（机械工程领域高质量科技期刊分级T1）

[7] 张越宏,袁昭成,黄锋飞,等. 不均衡下分类器评价辅助GAN的轴承故障诊断方法 [J/OL]. 中国测试, 1-8[2024-04-30]. http://kns.cnki.net/kcms/detail/51.1714.TB.20240325.1128.004.html.

[6] 周文军,肖晓萍,李自胜等. 基于改进长短期记忆网络的铣刀磨损量预测研究 [J]. 机床与液压, 2023, 51 (19): 203-210.

[5] 张越宏,袁昭成,王大龙等. 基于Abaqus软件的电梯制动器仿真分析研究 [J]. 中国电梯, 2023, 34 (09): 13-15.

[4] 简斌,肖晓萍,李自胜等. 机械设备多模态声源分离方法研究 [J]. 计算机技术与发展, 2023, 33 (06): 208-214.

[3] 康顺,李自胜,肖晓萍等. 基于改进NSGA-Ⅱ的汽油机标定优化研究 [J]. 车用发动机, 2023, (01): 52-61.

[2] 任建亭,汤宝平,雍彬等. 基于深度变分自编码网络融合SCADA数据的风电齿轮箱故障预警 [J]. 太阳能学报, 2021, 42 (04): 403-408. DOI:10.19912/j.0254-0096.tynxb.2018-1318.

[1] 邓佳林,邹益胜,张继冬等. 液压减振器故障-参数集的知识表达及映射 [J]. 机械设计与制造, 2020, (07): 46-50. DOI:10.19356/j.cnki.1001-3997.2020.07.012.

专利

[12] 张楷,赖旭伟,郑庆等. 一种非连续特性约束的铣刀磨损监测单源域泛化方法[P]. 四川省: CN117620774A,  2024-03-01.

[11] 张楷,黄锋飞,郑庆等. 一种轨道列车转向架滚动轴承故障诊断方法[P]. 四川省: CN117150304A, 2023-12-01.

[10] 张楷,郑庆,秦国浩等. 基于自适应对称损失的旋转机械噪声标签故障诊断方法[P]. 四川省: CN116502085A, 2023-07-28.

[9] 张楷,赖旭伟,郑庆等. 一种基于多参数引导空间注意力机制的铣刀磨损监测方法[P]. 四川省: CN115971970B, 2024-03-26.（专利号：ZL 2022 1 1534665.1)

[8] 张楷,丁国富,丁昆等. 一种基于伪标签传递式两阶段领域自适应的滚动轴承故障诊断方法[P]. 四川省: CN115876467A, 2023-03-31.

[7] 张楷,赖旭伟,郑庆等. 一种基于频率注意力机制的铣刀磨损监测方法[P]. 四川省: CN115771061B, 2024-03-26.（专利号：ZL 2022 1 1534664.7)

[6] 张越宏,袁昭成,王大龙等. 用于电梯制动器的紧急制动性能分析方法[P]. 四川省: CN117634266A, 2024-03-01.

[5] 张越宏,袁昭成,郑庆等. 电梯曳引机轴承故障诊断方法[P]. 四川省: CN116484258A, 2023-07-25.

[4] 郑庆,丁国富,张越宏等. 一种面向数字孪生应用的数据与模型融合方法[P]. 四川省: CN116204849A, 2023-06-02.

[3] 丁国富,谢家翔,郑庆等. 一种复杂装备全生命周期信息物理融合方法[P]. 四川省: CN115906006A, 2023-04-04.

[2] 张越宏,袁昭成,郑庆等. 制动力变化趋势监测装置及电梯杠杆鼓式制动器[P]. 四川省: CN217708438U, 2022-11-01.

[1] 郑庆,丁国富,张海柱等. 一种基于模型融合的复杂产品数字孪生构建与应用方法[P]. 四川省: CN114611313A, 2022-06-10.

论著之外的代表性研究成果和学术奖励

1.  张楷， 重庆市优秀博士论文, 重庆市教育委员会, 2022

2.  张楷， 重庆大学优秀博士论文, 重庆大学, 2022

3.  张楷(10)，詹天佑铁道科学技术奖创新团队奖，詹天佑科学技术发展基金会，2023

4.  Double Horizontal Jibs Gin Pole on Ground，IEEE国际标准，制定中，IEEE P3413 Double Horizontal Jibs Gin Pole on Ground - Home