卢森骧 - 东北大学 - 信息科学与工程学院

个人简介

教育经历 2007.9-2011.7东北大学信息科学与工程学院测控技术与仪器专业学士学位 2011.9-2013.7东北大学信息科学与工程学院电气工程专业硕士学位 2013.9-2018.7东北大学信息科学与工程学院控制理论与控制工程专业博士学位工作经历 2018.7-至今东北大学计算机科学与工程学院博士后 2018.7-2022.12 东北大学信息科学与工程学院讲师 2023.1-至今东北大学信息科学与工程学院副教授

研究领域

(1) 能源管道电磁无损检测 (2) 输变电设备故障诊断与健康管理 (3) 面向工业大数据的人工智能与深度学习

近期论文

查看导师新发文章（温馨提示：请注意重名现象，建议点开原文通过作者单位确认）

(1)Lu SX, Feng J, Zhang HG, et al. An Estimation Method of Defect Size From MFL Image Using Visual Transformation Convolutional Neural Network [J]. IEEE Transactions on Industrial Informatics, 2019, 15(1): 213-224. (2)Lu SX, Zhou TH, Wang CY, et al. An Internal Detector Positioning Method in Oil Pipelines Using Vibration Signal [J]. IEEE Sensors Journal, 2023, 23(12): 13411-13421. (3)Lu SX, Feng J, Li FM, et al. Precise Inversion for the Reconstruction of Arbitrary Defect Profiles Considering Velocity Effect in Magnetic Flux Leakage Testing [J]. IEEE Transactions on Magnetics, 2017, 53(4): 1-12. (4)Lu SX, Yue YQ, Liu XY, et al. A novel unbalanced weighted KNN based on SVM method for pipeline defect detection using eddy current measurements [J]. Measurement Science and Technology, 2023, 34(1): 1-9. (5)Hou DF, Lu SX, Yi GM, et al. A Target-Focusing Optimization Method for 3-D Profile Reconstruction of Defects Using MFL Measurements [J]. IEEE Transactions on Instrumentation and Measurement, 2023, 72: 1-11. (6)Feng J, Lu SX, Liu JH, et al. A Sensor Liftoff Modification Method of Magnetic Flux Leakage Signal for Defect Profile Estimation [J]. IEEE Transactions on Magnetics, 2017, 53(7): 1-13. (7)Feng J, Xiao Q, Lu SX, et al. A Double Remote Magnetic Field Synthesis Method for Reducing High-Speed MFL Signal Distortion Caused by Velocity Effect [J]. IEEE Transactions on Industrial Electronics, 2024, 71(1): 1049-1059. (8)Feng J, Zhang XB, Lu SX, et al. A Single-Stage Enhancement-Identification Framework for Pipeline MFL Inspection [J]. IEEE Transactions on Instrumentation and Measurement, 2022, 71: 1-13. (9)Feng J, Yao Y, Lu SX, et al. Domain Knowledge-Based Deep-Broad Learning Framework for Fault Diagnosis [J]. IEEE Transactions on Industrial Electronics, 2021, 68(4): 3454-3464. (10)Zhang XB, Feng J, Lu SX, et al. FMD-Framework: A Size Estimation Method for Pipeline Defects in Weld-Affected Zones [J]. IEEE Transactions on Instrumentation and Measurement, 2023, 72: 1-11. (11)Feng J, Li FM, Lu SX, et al. Injurious or Noninjurious Defect Identification From MFL Images in Pipeline Inspection Using Convolutional Neural Network [J]. IEEE Transactions on Instrumentation and Measurement, 2017, 66(7): 1883-1892. (12)Xiao Q, Feng J, Lu SX, et al. Accurate Identification for 3-D Position of Hybrid Defects in Ferromagnetic Pipe Using External Remote Field Eddy Current Testing [J]. IEEE Transactions on Magnetics, 2022, 58(3): 1-10. (13)Zhang SX, Feng J, Lu SX, et al. Weak Magnetic Flux Leakage Detection and Numerical Method Under Inclined Pressure Conditions [J]. IEEE Transactions on Magnetics, 2023, 59(8): 1-9. (14)Zhang SX, Feng J, Lu SX. A novel method for fusing graph convolutional network and feature based on feedback connection mechanism for nondestructive testing [J]. Pattern Recognition Letters, 2022, 164: 284-292. (15)Zhang SX, Feng J, Lu SX, et al. A novel MFL detection method based on low frequency AC magnetization for identification defect [J]. Journal of Magnetism and Magnetic Materials, 2023, 580: 1-11. (16)Zhang SX, Feng J, Lu SX, et al. A stress defect state measurement method based on low-frequency ACMFL excitation and Hall sensor array collection [J]. Measurement Science and Technology. 2023: 1-10 (17)卢森骧, 徐行, 张润江等. 基于多维度选择性搜索的小样本缺陷识别方法[J]. 仪器仪表学报, 2022, 43(01): 220-228 (18)卢森骧, 神祥凯, 张俊楠等. 基于三轴融合的漏磁内检测数据缺陷反演方法研究[J]. 仪器仪表学报, 2021, 42(12): 245-253