【1】Wang N, Jia L, Qin Y, et al. Dual-stage manifold preserving mixed supervised learning for bogie fault diagnosis under variable conditions[J]. Engineering Applications of Artificial Intelligence, 2025, 149: 110512.（中科院1区TOP）

【2】Wang N, Jia L, Zhang H, et al. Manifold-Contrastive Broad Learning System for Wheelset Bearing Fault Diagnosis [J]. IEEE Transactions on Intelligent Transportation Systems, 2023. DOI: 10.1109/TITS.2023.3274256. （智能交通领域顶刊）

【3】Wang N, Jia L, Qin Y, et al. Scale-independent Shrinkage Broad Learning System for High-speed Train Bearing Anomaly Detection under Variable Conditions [J]. Mechanical Systems and Signal Processing, 2023. DOI: 10.1016/j.ymssp.2023.110653.（机械工程领域顶刊，中科院TOP期刊，1区）

【4】Wang N, Kou L, Zhang H, et al. A self‐adaptive phase‐segmentation and health assessment framework for point machines[J]. IET Intelligent Transport Systems, 2022. DOI: 10.1049/itr2.12299. （中科院3区，智能交通领域权威期刊）

【5】Wang Z, Wang N, Zhang H, et al. et al. Segmentalized mRMR Features and Cost-sensitive ELM with Fixed Inputs for Fault Diagnosis of High-speed Railway Turnouts [J]. IEEE Transactions on Intelligent Transportation Systems, 2023. DOI: 10.1109/TITS.2023.3239636. （智能交通领域顶刊）

【6】Wang N, Wang Z, Jia L, et al. Adaptive multiclass Mahalanobis Taguchi system for bearing fault diagnosis under variable conditions[J]. Sensors, 2018, 19(1): 26. （JCR Q2，中科院3区，引用次数：20）

【7】Zuo Y, Wang N, Jia L, et al. Fully decomposed singular value and fixed dictionary extreme learning machine for bogie fault diagnosis[J]. IEEE Transactions on Intelligent Transportation Systems, 2021, 23(8): 10262-10274. (智能交通领域顶刊)

【8】Wang N, Wang H, Jia L, et al. Turnout health assessment based on dynamic time warping[C], Proceedings of the 4th International Conference on Electrical and Information Technologies for Rail Transportation (EITRT) 2019: Rail Transportation System Safety and Maintenance Technologies. Springer Singapore, 2020: 517-527. (EI)

【9】Wang N, Jia L, Wang Z. Bearing Fault Diagnosis Based on Adaptive Multiclass–Mahalanobis–Taguchi System[C]//2018 Prognostics and System Health Management Conference (PHM-Chongqing). IEEE, 2018: 1120-1125. (EI）