张法业
高级实验师
访问次数:
基本信息
  • 教师拼音名称:
    Zhang Faye
  • 电子邮箱:
    zhangfaye@sdu.edu.cn
  • 入职时间:
    2009-07-16
  • 所在单位:
    控制科学与工程学院
  • 学历:
    博士研究生毕业
  • 性别:
  • 联系方式:
    18660109051
  • 学位:
    博士
  • 在职信息:
    在职
  • 其他任职:
    中国仪器仪表学会青年工作委员会委员
  • 毕业院校:
    山东大学
  • 硕士生导师
教师简介

张法业,男,高级实验师,硕士生导师。现任中国仪器仪表学会青年工作委员会委员,中国光学学会光电技术专业委员会委员,山东省一流本科课程(虚拟仿真实验教学课程)负责人,国家级一流本科课程(虚拟仿真实验教学一流课程)主要成员,主要从事智能传感器与检测技术、装备智能故障诊断与寿命预测技术等研究,自主研发了温度、应变及振动等多参量监测传感器与仪器、数据融合分析与装备健康评估模型,研究成果在航空航天、轨道交通和能源化工等领域获得成功应用。近年来,承担国家重点研发计划、国家自然科学基金、省科技重大科技创新工程及企事业单位合作项目20余项,以第一作者或通讯作者发表故障诊断与寿命预测领域论文20余篇,行业领域顶级期刊论文2篇,申请发明专利15项,获山东省技术发明奖二等奖,中国自动化学会科技进步奖一等奖,山东省优秀科研成果奖二等奖,全国高等学校教师自制实验教学仪器设备创新大赛二等奖,山东省教学成果奖一等奖,中国仪器仪表学会高等教育教学成果奖二等奖,山东省自动化学会教学成果奖一等奖等10余项。

教育经历
  • 2013-9 — 2017-6
    山东大学
    检测技术与自动化装置
    工学博士学位
  • 2006-9 — 2009-6
    山东大学
    仪器仪表类其他专业
    硕士生
  • 2002-9 — 2006-7
    山东大学
    测控技术与仪器
    学士
工作经历
  • 2013-06-至今
    山东大学
  • 2009-07-至今
    山东大学
研究领域

智能传感器与装备健康监测、装备智能故障诊断与剩余寿命预测、深度学习、迁移学习

科研成果
论文

1.  姚鹏. Intelligent rolling bearing imbalanced fault diagnosis based on Mel-Frequency Cepstrum Coefficient and Convolutional Neural Networks.  Measurement,  112143, 2022. 

2.  王浩淼. Cross-domain open-set rolling bearing fault diagnosis based on feature improvement adversarial network under noise condition.  Journal of Intelligent & Fuzzy Systems,  46,  5073, 2024. 

3.  叶呈龙. Novel cross-domain fault diagnosis method based on model-agnostic meta-learning embedded in adaptive threshold network.  Measurement,  113677, 2023. 

4.  韩同卓. Novel adaptive loss weighted transfer network for partial domain fault diagnosis.  ISA Transactions,  362, 2024. 

5.  张法业. Intelligent rolling bearing compound fault diagnosis based on frequency-domain Gramian angular field and convolutional neural networks with imbalanced data.  JOURNAL OF VIBRATION AND CONTROL,  2023. 

6.  刘繁. Novel short-term low-voltage load forecasting method based on residual stacking frequency attention network.  Electric Power Systems Research,  110534, 2024. 

7.  刘福政. Fault diagnosis of rolling bearing combining improved AWSGMD-CP and ACO-ELM model.  Measurement,  112531, 2023. 

8.  李彦君. Bearing fault diagnosis method based on maximum noise ratio kurtosis product deconvolution with noise conditions.  Measurement,  113542, 2023. 

9.  王金喜. Maximum average impulse energy ratio deconvolution and its application for periodic fault impulses enhancement of rolling bearing.  Advanced Engineering Informatics,  101721, 2022. 

10.  刘福政. Structural discrepancy and domain adversarial fusion network for cross-domain fault diagnosis.  ADVANCED ENGINEERING INFORMATICS,  102217, 2023. 

11.  王浩淼. Multiscale convolutional conditional domain adversarial network with channel attention for unsupervised bearing fault diagnosis.  Proceedings of the Institution of Mechanical Engineers. Part I: Journal of Systems and Control Engineering,  238,  1123, 2024. 

12.  刘福政. Balance-blended adversarial distribution and smooth-suppressed labels refinement network for partial transfer fault diagnosis.  Engineering Applications of Artificial Intelligence,  135,  2024. 

13.  刘繁荣. Novel short-term low-voltage load forecasting method based on residual stacking frequency attention network.  Electr. Power Syst. Res,  233,  2024. 

14.  李彦君. Bearing fault diagnosis method based on maximum noise ratio kurtosis product deconvolution with noise conditions.  测量,  221,  2023. 

15.  王浩淼. Cross-domain open-set rolling bearing fault diagnosis based on feature improvement adversarial network under noise condition.  JOURNAL OF INTELLIGENT & FUZZY SYSTEMS,  46,  5073-5085, 2024. 

16.  王浩淼. Multiscale convolutional conditional domain adversarial network with channel attention for unsupervised bearing fault diagnosis.  PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART I-JOURNAL OF SYSTEMS AND CONTROL ENGINEERING,  2024. 

17.  张法业. Intelligent rolling bearing compound fault diagnosis based on frequency-domain Gramian angular field and convolutional neural networks with imbalanced data.  JVC/Journal of Vibration and Control,  2023. 

18.  王浩淼. Partial transfer learning method based on MDWCAN for rolling bearing fault diagnosis under noisy conditions.  PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEE,  2024. 

19.  何佳婕. Fault Diagnosis Method of Rolling Bearing Based on ESGMD-CC and AFSA-ELM.  SDHM Structural Durability and Health Monitoring,  18,  37-54, 2024. 

20.  叶呈龙. Novel cross-domain fault diagnosis method based on model-agnostic meta-learning embedded in adaptive threshold network.  测量,  222,  2023. 

21.  刘福政. Structural discrepancy and domain adversarial fusion network for cross-domain fault diagnosis.  ADVANCED ENGINEERING INFORMATICS,  58,  2023. 

22.  李伟. Impact Damage Identification of Aluminum Alloy Reinforced Plate Based on GWO-ELM Algorithm.  SDHM Structural Durability and Health Monitoring,  17,  485-500, 2023. 

23.  韩同卓. Novel adaptive loss weighted transfer network for partial domain fault diagnosis.  ISA Transactions,  2023. 

24.  韩同卓  and 张法业. Novel adaptive loss weighted transfer network for partial domain fault diagnosis.  ISA Transactions,  145,  362-372, 2023. 

25.  刘福政. Fault diagnosis of rolling bearing combining improved AWSGMD-CP and ACO-ELM model.  Measurement: Journal of the International Measurement Confederation,  209,  2023. 

26.  张法业. Intelligent rolling bearing compound fault diagnosis based on frequency-domain Gramian angular field and convolutional neural networks with imbalanced data.  Journal of Vibration and Control,  2023. 

27.  李伟. 基于深度置信网络铝合金加筋板冲击损伤识别.  《振动、测试与诊断》,  43,  88, 2023. 

28.  王金喜. Maximum average impulse energy ratio deconvolution and its application for periodic fault impulses enhancement of rolling bearing.  ADVANCED ENGINEERING INFORMATICS,  53,  2022. 

29.  姚鹏. Intelligent rolling bearing imbalanced fault diagnosis based on Mel-Frequency Cepstrum Coefficient and Convolutional Neural Networks.  Measurement: Journal of the International Measurement Confederation,  205,  2022. 

30.  张法业. Accuracy-Improved Bearing Fault Diagnosis Method Based on AVMD Theory and AWPSO-ELM Mode.  Measurement,  2021. 

31.  张艺蓝. Intelligent fault diagnosis of rolling bearing using the ensemble self-taught learning convolutional auto-encoders.  IET Science Measurement & Technology,  16,  130, 2022. 

专利
学生信息
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