学术成果：
一作或通讯作者发表论文：
[1] Zhao X, Zhu X, Yao J, et al. Intelligent Health Assessment of Aviation Bearing Based on Deep Transfer Graph Convolutional Networks under Large Speed Fluctuations[J]. Sensors, 2023, 23(9): 4379.
[2] Zhao X(赵孝礼), Yao J(姚建勇*), Deng W(邓文翔), et al. Intelligent Fault Diagnosis of Gearbox Under Variable Working Conditions With Adaptive Intraclass and Interclass Convolutional Neural Network[J]. IEEE Transactions on Neural Networks and Learning Systems, 2022, doi: 10.1109/TNNLS.2021.3135877. (SCI, IF=10.451, Top期刊, WOS:000740063500001 已发表)
[3] Zhao X(赵孝礼), Yao J(姚建勇*), Deng W(邓文翔), et al. Normalized Conditional Variational Auto-Encoder with adaptive Focal loss for imbalanced fault diagnosis of Bearing-Rotor system[J]. Mechanical Systems and Signal Processing, 2022, 170: 108826. (SCI, IF=6.823, Top期刊,WOS:000792759900004 已发表)
[4] Zhao X (赵孝礼), Yao J(姚建勇*), Deng W(邓文翔), Ding P (丁鹏), Zhuang J(庄集超), Liu Z(刘征). Multi-Scale Deep Graph Convolutional Networks for Intelligent Fault Diagnosis of Rotor-Bearing System Under Fluctuating Working Conditions[J]. IEEE Transactions on Industrial Informatics, 2022, doi: 10.1109/TII.2022.3161674. (SCI, IF=10.215, Top期刊, 已发表，WOS:000880654600019),
[5] X. Zhao, M. Jia and Z. Liu, "Semisupervised Graph Convolution Deep Belief Network for Fault Diagnosis of Electormechanical System With Limited Labeled Data," in IEEE Transactions on Industrial Informatics, vol. 17, no. 8, pp. 5450-5460, Aug. 2021, doi: 10.1109/TII.2020.3034189. (SCI, IF=11.648, Top期刊, 已检索，SCI收录WOS:000647406400030) (A类期刊)(ESI高被引论文)
[6] Zhao X(赵孝礼), Jia M(贾民平*), Ding P(丁鹏), et al. Intelligent Fault Diagnosis of Multi-Channel Motor-Rotor System based on Multi-manifold Deep Extreme Learning Machine[J]. IEEE/ASME Transactions on Mechatronics, 2020, 25(5): 2177-2187. (SCI，IF=5.303, Top期刊, 已检索，WOS:000578004900004)
[7] Zhao X(赵孝礼), Jia M(贾民平*), Liu Z(刘征). Multiple-Order Graphical Deep Extreme Learning Machine for Unsupervised Fault Diagnosis of Rolling Bearing, IEEE Transactions on Instrumentation and Measurement, 2021, 70:1-12. (SCI, IF=4.016, Top期刊，已检索，WOS:000681487400086)
[8] Zhao X(赵孝礼), Jia M(贾民平*), Liu Z(刘征). Semisupervised Deep Sparse Auto-Encoder With Local and Nonlocal Information for Intelligent Fault Diagnosis of Rotating Machinery, IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. 1-13, 2021, Art no. 3501413, doi: 10.1109/TIM.2020.3016045.[J]. IEEE Transactions on Instrumentation and Measurement, 2020, 70: 1-13. (SCI, IF=4.016, Top期刊，已检索，WOS:000591842200001)
[9] Zhao X(赵孝礼), Jia M(贾民平*), Lin M(林明耀). Deep Laplacian Auto-encoder and its application into imbalanced fault diagnosis of rotating machinery[J]. Measurement, 2020, 152: 107320. (SCI, IF=3.927, 已检索WOS:000508908600056.) (ESI 高被引论文)
[10] Zhao X(赵孝礼), Jia M(贾民平*). A novel unsupervised deep learning network for intelligent fault diagnosis of rotating machinery[J]. Structural Health Monitoring, 2020, 19(6):1745-1763. (SCI, IF=5.929, Top期刊, 已检索，WOS:000507188200001)
[11] Zhao X(赵孝礼), Jia M(贾民平*). Fault diagnosis of rolling bearing based on feature reduction with global-local margin Fisher analysis[J]. Neurocomputing, 2018, 315: 447-464. (SCI, IF=5.719, 已检索，WOS:445934400041)
[12] Zhao X(赵孝礼), Jia M(贾民平*). A new Local-Global Deep Neural Network and its application in rotating machinery fault diagnosis[J]. Neurocomputing, 2019, 366(13): 215-233. (SCI, IF=5.719, 已检索， WOS:000488202500021 )
[13] Zhao X(赵孝礼), Jia M(贾民平*). A Novel Deep Fuzzy Clustering Neural Network Model and Its Application in Fault Recognition of Rolling Bearing[J]. Measurement Science and Technology, 2018, 29: 125005 (21pp). (SCI, IF =2.046, 已检索，WOS:000449152500005)
[14] Zhao X(赵孝礼), Jia M(贾民平*), Ding P(丁鹏), Liu Z(刘征), et al. A new intelligent weak fault recognition framework for rotating machinery [J]. International Journal of Acoustics and Vibration, 2020, 25(3): 461-479. ( SCI, IF=0.581, 已检索，WOS:000576373600017)
[15] Zhao X, Jia M, Liu Z. Fault diagnosis framework of rolling bearing using adaptive sparse contrative auto-encoder with optimized unsupervised extreme learning machine[J]. IEEE Access, 2019, 8: 99154-99170.
[16] 赵孝礼, 赵荣珍*. 全局与局部判别信息融合的转子故障数据集降维方法研究[J]. 自动化学报, 2017, 43(4): 560-567. ( EI期刊, 国内一级学报, 已检索，Accession number: 20172403755187).
[17] 赵孝礼, 赵荣珍*, 孙业北, 何敬举. 基于正则化核最大边界投影维数约简的滚动轴承故障诊断[J]. 振动与冲击, 2017, 36(14):104-110. ( EI, 期刊, 已检索，Accession number: 20174204281960).
[18] Zhao X(赵孝礼), Zhao R (赵荣珍*). A Method to Integrate KSSOMFA and WKNN Together on Faults Identification of Rotating Machinery[C]. 2016 13th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI). IEEE216675-680. (EI, 国际会议, 已检索，Accession number: 20172403755187)
[19] Zhao X(赵孝礼), Liu Z(刘征*), Wang T(王腾), Bin J(宾俊驰), M. Jia (贾民平), Unsupervised Fault Diagnosis of Machine via Multiple-Order Graphical Deep Extreme Learning Machine [C]. The 9th Asia-Pacific International Symposium on Advanced Reliability and Maintenance Modeling, 2020: 1-6. (APARM 2020 – Vancouver). (EI, 国际会议, 已检索)
[20] Zhao X(赵孝礼), Yao J(姚建勇*), Deng W(邓文翔), et al. Imbalanced Fault Diagnosis of Bearing-Rotor System via Normalized Conditional Variational Auto-Encoder with Adaptive Focal Loss[C]//2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing). IEEE, 2021: 1-6. (EI, 国际会议, 已检索，Accession number: 20220511542295)
[21] 赵荣珍, 赵孝礼, 何敬举，刘韵佳，相关流形距离在转子故障数据集分类中的应用方法[J]. 振动与冲击, 2017, 36(18):125-130. ( EI期刊, 已检索, Accession number: 20174804454428导师一作，学生二作).
[22] 赵孝礼,姚建勇,邓文翔等.“数智化”背景下高校兵器类专业教学新模式改革浅析[J].科技风,2023(07):98-100.DOI:10.19392/j.cnki.1671-7341.202307032.
[23] 赵孝礼. 全局与局部特征信息融合的旋转机械故障数据集降维方法研究[D].兰州理工大学,2017.
[24] 赵孝礼. 基于图嵌入自编码的滚动轴承故障诊断方法研究[D].东南大学,2021.DOI:10.27014/d.cnki.gdnau.2021.000057.
合作成果：
[1] Zhuang, J., Cao, Y., Jia, M., Zhao, X., & Peng, Q. (2023). Remaining useful life prediction of bearings using multi-source adversarial online regression under online unknown conditions[J]. Expert Systems with Applications, 2023, 227: 120276.
[2] Zhuang, J., Cao, Y., Jia, M., Zhao, X., & Peng, Q. (2023). Fault diagnosis of bearings using a two-stage transfer alignment approach with semantic consistency and entropy loss[J]. Expert Systems with Applications, 2023, 226: 120274.
[3] Cao Y, Jia M, Zhao X (赵孝礼), et al. Semi-supervised machinery health assessment framework via temporal broad learning system embedding manifold regularization with unlabeled data[J]. Expert Systems with Applications, 2023, 222: 119824.
[4] Y Cao, M Jia, Y Ding, X Zhao(赵孝礼), P Ding, L Gu et al. Complex domain extension network with multi-channels information fusion for remaining useful life prediction of rotating machinery[J]. Mechanical Systems and Signal Processing, 2023, 192: 110190.
[5] P. Ding, M. Jia, Y. Ding, Y. Cao, J. Zhuang and X. Zhao(赵孝礼), "Machinery Probabilistic Few-Shot Prognostics Considering Prediction Uncertainty," in IEEE/ASME Transactions on Mechatronics, doi: 10.1109/TMECH.2023.3270901.
[6] Y. Ding, M. Jia, Y. Cao, X. Yan, X. Zhao(赵孝礼) and C. -G. Lee, "Unsupervised Fault Detection With Deep One-Class Classification and Manifold Distribution Alignment," in IEEE Transactions on Industrial Informatics, doi: 10.1109/TII.2023.3275696.
[7] She, D., Chen, J., Yan, X., Zhao, X.(赵孝礼), & Pecht, M. (2023). Diversity maximization-based transfer diagnosis approach of rotating machinery[J]. Structural Health Monitoring, 2023: 14759217231164921.
[8] Zhuang, J., Cao, Y., Jia, M., Zhao, X.(赵孝礼), & Peng, Q. (2023). Fault diagnosis of bearings using a two-stage transfer alignment approach with semantic consistency and entropy loss[J]. Expert Systems with Applications, 2023: 120274.
[9] Zhuang, J., Cao, Y., Jia, M., Zhao, X.(赵孝礼), & Peng, Q. (2023). Remaining Useful Life Prediction of Bearings Using Multi-Source Adversarial Online Regression Under Online Unknown Conditions. Expert Systems with Applications, 120276.
[10] Y. Cao, J. Zhuang, M. Jia, X. Zhao(赵孝礼), X. Yan and Z. Liu, "Picture-in-Picture Strategy-Based Complex Graph Neural Network for Remaining Useful Life Prediction of Rotating Machinery," in IEEE Transactions on Instrumentation and Measurement, vol. 72, pp. 1-11, 2023, Art no. 2511311, doi: 10.1109/TIM.2023.3268456.
[11] Weng, C., Lu, B., Gu, Q., & Zhao, X.(赵孝礼) (2023). A novel hierarchical transferable network for rolling bearing fault diagnosis under variable working conditions[J]. Nonlinear Dynamics, 2023: 1-20.
[12] Ding, Y., Jia, M., Cao, Y., Ding, P., Zhao, X.(赵孝礼), & Lee, C. G. (2023). Domain generalization via adversarial out-domain augmentation for remaining useful life prediction of bearings under unseen conditions. Knowledge-Based Systems, 261, 110199.
[13] C. Weng, B. Lu, Q. Gu and X. Zhao(赵孝礼), "A Novel Multisensor Fusion Transformer and Its Application Into Rotating Machinery Fault Diagnosis," in IEEE Transactions on Instrumentation and Measurement, vol. 72, pp. 1-12, 2023, Art no. 3507512, doi: 10.1109/TIM.2023.3244822.
[14] Ding, Y., Jia, M., Zhuang, J., Cao, Y., Zhao, X.(赵孝礼), & Lee, C. G. (2023). Deep imbalanced domain adaptation for transfer learning fault diagnosis of bearings under multiple working conditions. Reliability Engineering & System Safety, 230, 108890.
[15] Xu, Y., Yan, X., Feng, K., Zhang, Y., Zhao, X.(赵孝礼),, Sun, B., & Liu, Z. Global contextual multiscale fusion networks for machine health state identification under noisy and imbalanced conditions[J]. Reliability Engineering & System Safety, 2023, 231: 108972.
2022
[16] Cao, Y., Jia, M., Zhuang, J., & Zhao, X. (赵孝礼)(2022). Research on sparsity measures for rotating machinery health monitoring[J]. Journal of Mechanical Science and Technology, 2022, 36(12): 5831-5843.
[17] Wu, D., Jia, M., Cao, Y., Ding, P., & Zhao, X. (赵孝礼)(2022). Remaining useful life estimation based on a nonlinear Wiener process model with CSN random effects[J]. Measurement, 2022, 205: 112232.
[18] Zhuang, J., Jia, M., Cao, Y., & Zhao, X.(赵孝礼) (2022). Semi-supervised double attention guided assessment approach for remaining useful life of rotating machinery[J]. Reliability Engineering & System Safety, 2022, 226: 108685.
[19] Zhuang J, Jia M, Zhao X. (赵孝礼), An adversarial transfer network with supervised metric for remaining useful life prediction of rolling bearing under multiple working conditions[J]. Reliability Engineering & System Safety, 2022, 225: 108599.
[20] Cao, Y., Jia, M., Ding, P., Zhao, X.,(赵孝礼) & Ding, Y. (2022). . Incremental learning for remaining useful life prediction via temporal cascade broad learning system with newly acquired data[J]. IEEE Transactions on Industrial Informatics, 2022.
[21] Ding, P., Jia, M., Ding, Y., Cao, Y., & Zhao, X. (2022). Intelligent machinery health prognostics under variable operation conditions with limited and variable-length data[J]. Advanced Engineering Informatics, 2022, 53: 101691.
[22] DingP, JiaM, Zhuang J, DingY, Cao Y, Zhao X(赵孝礼)Multiobjective Evolution Enhanced Collaborative Health Monitoring and Prognostics: A Case Study of Bearing Life Test With Three-Axis Acceleration Signals[J]. IEEE Transactions on Instrumentation and Measurement, 2022, 71: 1-12. (SCI);
[23] J. Zhuang, M. Jia, Y. Ding and X. Zhao(赵孝礼), Health assessment of rotating equipment with unseen conditions using adversarial domain generalization toward self-supervised regularization learning[J]. IEEE/ASME Transactions on Mechatronics, 2022, 27(6): 4675-4685, doi: 10.1109/TMECH.2022.3163289.
[24] Ding Y, Ding P, Zhao X(赵孝礼), et al. Transfer learning for remaining useful life prediction across operating conditions based on multisource domain adaptation[J]. IEEE/ASME Transactions on Mechatronics, 2022, 27(5): 4143-4152.
2021
[25] DingP, JiaM, ZhaoX(赵孝礼), Meta deep learning based rotating machinery health prognostics toward few-shot prognostics[J]. Applied soft computing, 2021, 104: 107211.(SCI, 已发表);
[26] DingP, JiaM, DingY, ZhaoX(赵孝礼). Statistical alignment based meta gated recurrent unit for cross-domain machinery degradation trend prognostics using limited data[J]. IEEE Transactions on Instrumentation and Measurement, 2021, 70: 1-12. (SCI，已发表);
2020之前
[27] ShaoJ, NiuY, XueC, WuQ, ZhouX, XieY, ZhaoX(赵孝礼). Single-channel SEMG using wavelet deep belief networks for upper limb motion recognition[J]. International Journal of Industrial Ergonomics, 2020, 76: 102905. (SCI, 已检索)
[28] WangT, LiuZ,ZhaoX(赵孝礼), LiaoM, MradN. Bayesian-Based Method for the Remaining Useful Life and Reliability Prediction of Steel Structure[C]. The 9th Asia-Pacific International Symposium on Advanced Reliability and Maintenance Modeling (APARM 2020 –Vancouver) (国际会议, EI 已检索)
[29] 赵荣珍, 数据驱动途径的典型旋转机械智能故障决策知识粒计算问题研究. 甘肃省,兰州理工大学,2021-03-29.
[30] 何敬举,赵荣珍,赵孝礼等.基于邻域粗糙集的转子故障数据属性约简[J].机械设计与制造工程,2018,47(03):22-26.
[31] 赵荣珍,赵孝礼,何敬举等.相关流形距离在转子故障数据集分类中的应用方法[J].振动与冲击,2017,36(18):125-130+139.DOI:10.13465/j.cnki.jvs.2017.18.019.