个人简介
教育背景与工作(挂职)经历:
时间
毕业院校
学历
2008.09-2012.07
华北电力大学科技学院
本科
2012.09-2015.04
华北电力大学机械工程学院
硕士
2015.09-2019.03
东南大学机械工程学院
博士
2019.06-至今
南京林业大学机械电子工程学院
讲师
科研项目:
序号
项目名称
项目性质
起止年度
1
基于自适应时频分析的旋转机械故障诊断方法及应用研究
江苏省普通高校研究生科研创新计划项目(主持)
2017.07-2018.12
2
融合疲劳现象学与奇异谱分解的起重机损伤识别及寿命预测研究
国家自然科学基金(参与)
2017.01-2020.12
3
基于传感器优化布置与信息融合的质量监控研究
国家自然科学基金(参与)
2011.01-2013.12
4
机-网耦合作用下风力发电机传动系统故障机理与诊断方法研究
国家自然科学基金(参与)
2015.01-2018.12
5
2MW风力发电机组振动测试分析
校企合作项目(参与)
2016.03-2016.05
6
智能电机运行状态监测与维护系统开发
校企合作项目(参与)
2017.07-2018.07
教学工作:
机电装备设计,数控技术
荣誉奖励:
2018.10 博士研究生国家奖学金
2018.03 江苏省省级三好研究生
2017.10 博士研究生国家奖学金
2016.12 河北省优秀硕士学位论文
2015.03 华北电力大学优秀毕业生
2014.10 硕士研究生国家奖学金
2011.09 河北省大学生数学竞赛一等奖
2009.10 国家励志奖学金
研究领域
1.机械装备工况监测与故障诊断
2.工程结构损伤机理、损伤识别/定位
3.自适应滤波、降噪、时频分析技术
4.滚动轴承健康状态评估与寿命预测
近期论文
查看导师最新文章
(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)
Xiaoan Yan, Minping Jia. Application of CSA-VMD and optimal scale morphological slice bispectrum in enhancing outer race fault detection of rolling element bearings[J]. Mechanical Systems and Signal Processing, 2019, 122: 56-86. (SCI收录,top )
Xiaoan Yan, Minping Jia. Wan Zhang, Lin Zhu. Fault diagnosis of rolling element bearing using a new optimal scale morphology analysis method[J]. ISA transactions, 2018, 73: 165-180. (SCI收录)
Xiaoan Yan, Minping Jia. Intelligent fault diagnosis of rotating machinery using improved multiscale dispersion entropy and mRMR feature selection[J]. Knowledge-Based Systems, 2019, 163: 450-471. (SCI收录)
Xiaoan Yan, Ying Liu, Minping Jia. Research on an enhanced scale morphological-hat product filtering in incipient fault detection of rolling element bearings[J]. Measurement, 2019, 147: 106856. (SCI收录)
Xiaoan Yan, Minping Jia. A novel optimized SVM classification algorithm with multi-domain feature and its application to fault diagnosis of rolling bearing[J]. Neurocomputing, 2018, 313: 47-64. (SCI收录)
Xiaoan Yan, Minping Jia, Ling Xiang. Compound fault diagnosis of rotating machinery based on OVMD and a 1.5-dimension envelope spectrum[J]. Measurement Science and Technology, 2016, 27(7): 075002. (SCI收录)
Xiaoan Yan, Minping Jia, Zhuanzhe Zhao. A novel intelligent detection method for rolling bearing based on IVMD and instantaneous energy distribution-permutation entropy[J]. Measurement, 2018, 130, 435-447. (SCI收录)
Xiaoan Yan, Ying Liu, Minping Jia, Yinlong Zhu. A Multi-Stage Hybrid Fault Diagnosis Approach for Rolling Element Bearing Under Various Working Conditions[J]. IEEE Access, 2019, 7: 138426-138441. (SCI收录)
Xiaoan Yan, Ying Liu, Minping Jia. A Feature Selection Framework-Based Multiscale Morphological Analysis Algorithm for Fault Diagnosis of Rolling Element Bearing[J]. IEEE Access, 2019, 7: 123436-123452. (SCI收录)
Xiaoan Yan, Minping Jia. Fault detection for rolling element bearing using an enhanced morphological-hat product filtering method[C]. IOP Conference Series: Materials Science and Engineering, 2018, 394(3): 032066. (EI收录)
鄢小安, 贾民平. 基于改进奇异谱分解的形态学解调方法及其在滚动轴承故障诊断中的应用[J]. 机械工程学报, 2017, 53(7):104-112. (EI收录)
鄢小安, 贾民平. 自适应多尺度开闭平均-hat变换及在轴承故障诊断中的应用[J]. 东南大学学报:自然科学版,2019. (EI收录)
Lin Xiang, Xiaoan Yan. A self-adaptive time-frequency analysis method based on local mean decomposition and its application in defect diagnosis[J]. Journal of Vibration and Control, 2016, 22(4): 1049-1061. (SCI收录)
Aijun Hu, Xiaoan Yan, Lin Xiang. A new wind turbine fault diagnosis method based on ensemble intrinsic time-scale decomposition and WPT-fractal dimension[J]. Renewable energy, 2015, 83: 767-778. (SCI收录)
向玲, 鄢小安. 基于集成固有时间尺度分解和谱峭度的滚动轴承故障检测[J]. 中南大学学报(自然科学版), 2016, 47(07): 2273-2280. (EI收录)
向玲, 鄢小安. 汽轮机转子故障诊断中LMD法和EMD法的性能对比研究[J]. 动力工程学报, 2014, 34(12): 945-951. (EI收录)
向玲, 鄢小安. 基于小波包的EITD风力发电机组齿轮箱故障诊断[J]. 动力工程学报, 2015, 35(03): 205-212. (EI收录)
Xianbo Wang, Zhixin Yang, Xiaoan Yan. Novel particle swarm optimization-based variational mode decomposition method for the fault diagnosis of complex rotating machinery[J]. IEEE/ASME Transactions on Mechatronics, 2018, 23(1): 68-79. (SCI收录)
Wan Zhang, Minping Jia, Xiaoan Yan, Lin Zhu. Weighted sparsity-based denoising for extracting incipient fault in rolling bearing[J]. Journal of Mechanical Science and Technology, 2017, 31(10): 4557-4567. (SCI收录)
Wan Zhang, Minping Jia, Lin Zhu, Xiaoan Yan. Comprehensive overview on computational intelligence techniques for machinery condition monitoring and fault diagnosis[J]. Chinese Journal of Mechanical Engineering, 2017, 30(4): 782-795. (SCI收录)