金年会信誉至上

2020

1.Zhao X, Jia M*, Ding P, Yang C, She D and Liu Z. 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. doi: 10.1109/TMECH.2020.3004589

2.Zhao X, Jia M*. A novel unsupervised deep learning network for intelligent fault diagnosis of rotating machinery[J]. Structural Health Monitoring, 2020, 19(6):1745-1763. WOS:000507188200001

3.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, 2020, 8: 99154-99170. (WOS:000541127800018) 

4.She D, Peng N, Jia M*, Michael Pecht. Wasserstein distance based deep multi-feature adversarial transfer diagnosis approach under variable working conditions [J]. Journal of Instrumentation, 2020, 15(06): P06002. (WOS:000545350900002)

5.Ding P, Jia M*, Wang H, A dynamic structure-adaptive symbolic approach for slewing bearings’ life prediction under variable working conditions[J]. Structural Health Monitoring, 2020, online: https://doi.org/10.1177/1475921720929939 (WOS:000542724700001)

6.Mao Y, Jia M*, Yan X. A new bearing weak fault diagnosis method based on improved singular spectrum decomposition and frequency-weighted energy slice bispectrum[J]. Measurement, 2020, 166:108235. ( WOS:000577288400056)

7.She D, Jia M*, Michael Pecht. Sparse auto-encoder with regularization method for health indicator construction and remaining useful life prediction of rolling bearing [J]. Measurement Science and Technology,31(2020)105005. (WOS:000560071600001)

8.Yang, C, Jia M*. Health condition identification for rolling bearing based on hierarchical multiscale symbolic dynamic entropy and least squares support tensor machine-based binary tree[J]. Structural Health Monitoring, 2020, https://doi.org/10.1177/1475921720923973 (WOS: 000538787400001)

9.Zhao X, Jia M*, Ding P, et. al. A new intelligent weak fault recognition framework for rotating machinery[J]. International Journal of Acoustics and Vibration, 2020, 25(3): 461-479. ( WOS:000576373600017.)

10.Xiaoli Zhao, Minping Jia, Mingyao Lin. Deep Laplacian Auto-encoder and its application into imbalanced fault diagnosis of rotating machinery. Measurement, 2020, 152: 107320

2019

1.Yan X, Jia M. Intelligent fault diagnosis of rotating machinery using improved multiscale dispersion entropy and mRMR feature selection[J]. Knowledge-Based Systems, 2019, 163(1):450-471（WOS:000454468200035）

2.Yan X, Jia M. 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二区，WOS:000457948600004）高被引

3.Yan X, Liu Y, Jia M. A Feature Selection Framework-Based Multiscale Morphological Analysis Algorithm for Fault Diagnosis of Rolling Element Bearing[J]. IEEE Access, 2019, 7: 123436-123452. （WOS:000487833000009）

4.Yan X, Liu Y, Jia M. Research on an enhanced scale morphological-hat product filtering in incipient fault detection of rolling element bearings[J]. Measurement, 2019, 147: 106856.（WOS:000487249900050）

5.Yan X, Jia M. Improved singular spectrum decomposition-based 1.5-dimensional energy spectrum for rotating machinery fault diagnosis[J]. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 2019, 41(1): 50. （WOS:000454915100001）

6.杨诚,贾民平.基于Volterra-PARAFAC模型的滚动轴承故障诊断方法[J].金年会信誉至上学报(自然科学版),2019,49(04):742-748. 

7.Yang C, and Jia M. A novel weak fault signal detection approach for a rolling bearing using variational mode decomposition and phase space parallel factor analysis. Measurement Science and Technology. 2019. 30(11): 115004.（WOS:000484511900002）

8.She D, Jia M. Wear indicator construction of rolling bearings based on multi-channel deep convolutional neural network with exponentially decaying learning rate [J].Measurement, 135(2019)368-375. (WOS:000468747300038，EI收录，检索号：20184906174141) （WOS:000468747300038）

9.She D, and Jia M, Health Indicator Construction of Rolling Bearings Based on Deep Convolutional Neural Network Considering Phase Degradation, 2019 Prognostics and System Health Management Conference (PHM-Paris), Paris, France, 2019, pp. 373-378. (WOS:000485048900063)

10.Zhao XL, Jia M. A new Local-Global Deep Neural Network and its application in rotating machinery fault diagnosis[J]. Neurocomputing, 366 (2019) 215-233.（SCI二区，WOS:000488202500021）

11.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. 金年会信誉至上学报，2019, 49（5）:826-832

2018

1.Yan XA, Jia MP, Zhang W, Zhu L. Fault diagnosis of rolling element bearing using a new optimal scale morphology analysis method[J]. ISA transactions, 2018, 73: 165-180.（WOS: 000427664100016）表现不俗

2.Yan XA, Jia MP. 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（WOS: 000443150900005）表现不俗

3.Zhao XL, Jia MP. A novel deep fuzzy clustering neural network model and its application in rolling bearing fault recognition[J]. Measurement Science and Technology. 2018，29：125005. （WOS:000449152500005）

4.Yan XA, Jia MP, Zhao Z. A novel intelligent detection method for rolling bearing based on IVMD and instantaneous energy distribution-permutation entropy[J]. Measurement, 2018, 130, 435-447.（WOS:000446464400041）表现不俗

5.Zhao XL, Jia MP. Fault diagnosis of rolling bearing based on feature reduction with global-local margin Fisher analysis[J]. Neurocomputing, 315 (2018) 447-464. （WOS: 000445934400041）

6.Luo, Cheng; Jia, MinPing; Wen, Yue . The Diagnosis Approach for Rolling Bearing Fault based on Kurtosis Criterion EMD and Hilbert Envelope Spectrum. 3rd IEEE Information Technology and Mechatronics Engineering Conference (ITOEC): Chongqing, PEOPLES R CHINA, OCT 03-05, 2017 ( WOS:000422907300139)

7.Yan, Xiaoan, Jia, Minping. Fault detection for rolling element bearing using an enhanced morphological-hat product filtering method. IOP Conference Series: Materials Science and Engineering, v 394, n 3, August 8, 2018, 2018 5th International Conference on Advanced Composite Materials and Manufacturing Engineering

8.Yan XA, Jia MP. 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.

9.Yan XA, Jia MP, She DM, Zhao XL. Application of Improved Singular Spectrum Decomposition in Crane Structural Damage Detection[C]. The 4st International Conference on Structural Health Monitoring and Integrity Management, Hangzhou, China, 2018. 10.

10.鄢小安, 贾民平. 基于奇异谱分解-形态包络排列熵的滚动轴承故障诊断[C]. 第十二届全国振动理论及应用学术会议, 广西南宁, 2017, 10.

11.赵孝礼, 贾民平, 鄢小安, 佘道明. 基于能量相关奇异谱分解的起重机主梁结构损伤识别方法[J]. 振动与冲击, 2018, 37 (s) 199-201.

12.佘道明, 贾民平, 张菀. 一种新型深度自编码网络的滚动轴承健康评估方法[J].金年会信誉至上学报(自然科学版), 2018,48(05):801-806.

2010-2017

1.Zhu L, Jia MP. Estimation study of structure crack propagation under random load based on multiple factors correction. J Braz Soc Mech Sci Eng, 2017,39: 681-693. WOS:000394173100004

2.Zhu L, Jia MP. A new approach for the influence of residual stress on fatigue crack propagation. Results in Physics, 2017, 7: 2204-2212.

3.朱林，贾民平，冯月贵等.考虑残余应力重分布情况下的裂纹扩展预测研究[J]. 机械工程学报，2017，53（8）：43-49. (EI一级学报)

4.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(07):104-112.)

5.Zhu L, Jia MP, Jiang CC, et al. Estimation of structure crack propagation based on multiple factors correction. Journal of Southeast University (English Edition), 2017, 33 (1): 39-45. (EI核心版)

6.Zhang W, Jia MP, Zhu L, et al. Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis. Chinese Journal of Mechanical Engineering, 2017,30(4): 782-795 .WOS:000404646100003

7.Zhang W, Jia M, Yan X, et al. Weighted sparsity-based denoising for extracting incipient fault in rolling bearing. Journal of Mechanical Science and Technology, 2017, 31(10): 4557-4567. WOS:000415983500002

8.Yan X, Jia M, Xiang L. 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.

9.鄢小安,贾民平. 参数优化的组合形态-hat变换及其在风力发电机组故障诊断中的应用[J]. 机械工程学报, 2016,52(13):103-110.

10.Zhuanzhe Zhao, Qingsong Xu, Minping Jia. Improved shuffled frog leaping algorithm-based BP neural  network and its application in bearing early fault diagnosis. Neural Comput & Applic, 2016, 27(2), 375-385 

11.张菀，贾民平. 自适应Morlet小波变换方法及其在滚动轴承早期故障特征提取中的应用. 金年会信誉至上学报，2016, 46(3): 457-463.

12.He Kang，Xu Qingsong, JIA Minping. Optimal Sensor Deployment for Manufacturing Process Monitoring Based on Quantitative Cause-Effect Graph，IEEE Transactions on Automation Science and Engineering，2016，13（3）：1-5 

13.贾民平; 周浩; 杨小兰; 刘极峰; 汪震. 双质体振动磨动力学建模及参数优化. 振动与冲击, 2016   Dynamic modeling and parametric optimization for a double-mass vibration mill，Accession number: 20162202444363

14.沈彦佑，贾民平，朱林. 离心式吸叶机噪声分析及其长短叶片优化研究. 振动与冲击，2016 

15.Kang He, Qingsong Xu, Minping Jia. Modeling and Predicting Surface Roughness in Hard Turning  Using a Bayesian Inference-Based HMM-SVM Model. IEEE Transactions On Automation Science And Engineering，2015,12（3）: 1092-1103 WOS:000358585200027

16.He Kang, Jia Minping, Xu Qingsong. Optimal Sensor Deployment for Manufacturing Process  Monitoring Based on Quantitative Cause-Effect Graph[J]. IEEE Transactions on Automation Science and Engineering, 2015, 99:1-13 

17.Zhao, Zhuanzhe; Xu, Qingsong; Jia, Minping. Sensor network optimization of gearbox based on  dependence matrix and improved discrete shuffled frog leaping algorithm. Natural Computing, 2015，（4）：121-126 

18.Junai Zhao, Minping Jia. Segmentation algorithm for small targets based on small targets based on improved datafield and fuzzy c-means clustering [J]. Optik, 2015, 126(23):4330~4336

19.贾民平, 韩冰. 改进VPMCD法及其在机械故障诊断中的应用. 中国机械工程, 26(14): 1861 -1865, 2015

20.朱林，石光林，张菀 贾民平. 基于多因素修正的结构件疲劳寿命预估方法， 金年会信誉至上学报(自然科学版)，2015，45（3）：469-473

21. Xu, Qingsong，Jia, Minping. Model Reference Adaptive Control With Perturbation Estimation for a Micropositioning System，IEEE Transactions on Control Systems Technology，2014, 22(1):352-359

22.何康，贾民平，赵转哲.  基于属性层次模型的单工位状态监测异类传感器布置优化[J]. 机械工程学报, 2014, 50(24):17-23 (EI) 

23.赵君爱，贾民平. 工件表面微小缺陷的检测与识别方法.  金年会信誉至上学报,  2014，（04）：735-739

24.离心式吸叶机噪声分析及其长短叶片优化研究双质体振动磨动力学建模及参数优化

25.Jia, Yukun, Jia, Minping; Xu, Qingsong . A dual-axis electrostatically driven MEMS microgripper. International Journal of Advanced Robotic Systems, v 11, November 24, 2014 (EI: 20144900299869) （SCI）

26.Jiang, Yu, Xu, Feiyun. Xu, Bingsheng, Jia, Minping, Hu, Jianzhong,Gallego, Antolino. Simulation and experimental investigation on the AE tomography to improve AE source location in the concrete structure. Mathematical Problems in Engineering, v 2014, 2014 （SCI）

27.Ziqiang Chi, Minping Jia, Qingsong Xu. Fuzzy PID Feedback Control of Piezoelectric Actuator with Feedforward Compensation.  Mathematical Problems in Engineering, v 2014, 2014 （EI: 20144900274847） （SCI）

28.Xu, Qingsong, Wong, Pak-Kin, Jia, Minping, Zhang, Chengjin, Yen, Ping-Lang. Engineering applications of intelligent monitoring and control 2014. Mathematical Problems in Engineering, January 15, 2015, EI: 20150600493828 （SCI）

29.Kang He, Minping Jia, Jiangzhong Hu, Zhuanzhe Zhao. Quality Monitoring Method Based on Tool Vibrations and the Discrete Hidden Markov Model at Various Cutting Parameters in Hard Turning, Journal of Computational and Theoretical Nanoscience 06/2013; 19(6):1636-1640. DOI:10.1166/asl.2013.4545  （SCI）1.25 Impact Factor

30.Wang, Haijun, Xu, Feiyun, Zhao, Jun'ai, Jia, Minping, Hu, Jianzhong, Huang, Peng. Bispectrum feature extraction of gearbox faults based on nonnegative Tucker3 decomposition with 3D calculations. Chinese Journal of Mechanical Engineering (English Edition),  26(6):1182-1193, 2013 (EI: 20135217144131) （SCI）

31.Zhuanzhe Zhao, Minping Jia, Kang He. Approach of Combination of Weighted Evidence Based on Evidential Closeness Degree and Its Application. Advanced Materials Research, 2013,630:377-382  (EI: 20130615997084)

32.Kang He, Minping Jia, Zhuanzhe Zhao. Quality Monitoring of Surface Roughness and Roundness Using Hidden Markov Model. Advanced Materials Research, 2013,630:308-341 (EI:20130615997070)

33.Kang He, Minping Jia, Zhuanzhe Zhao, Rong Wang. Sensor Optimization for Cutting Status Monitoring in Single Manufacturing Unit. Advanced Materials Research, 2012(569) :636-639 (EI:20124715701649)

34.Xiaolan Yang, Minping Jia, Jingchao Zou,et al. Frequency Conversion Control for Vibration Mill with High Vibration Intensity Based on Multi-Wave Variable Sinusoid, Advanced Materials Research, 2012 (538-541):2504-2507 (EI: 20130816038284)

35.高清清, 贾民平. 基于 EEMD 的奇异谱熵在旋转机械故障诊断中的应用[J]. 金年会信誉至上学报，2011，41（5）：998~1001 (EI:20114414475025)

36.王荣，贾民平，刘桂兴. 状态监测振动传感器优化布置理论及应用[J]. 金年会信誉至上学报，2011，41（1）：77~81 (EI: 20111213768313)

37.王恒，贾民平，陈左亮等. 135MW球磨机制粉系统的优化试验研究与分析[J]. 矿山机械，2011，39（3）:71~75

38.王恒，贾民平，陈左亮. 基于软测量的球磨机优化控制方法[J]. 控制工程，2011，18（5）:762~766

39.Peng Huang, Minping Jia, Binglin Zhong. New Method to Measure the Fill Level of the Ball Mill I-Theoretical Analysis and DEM Simulation[J], Chinese Journal of Mechanical Engineering, 23(4), pp 460-467,  2010.(EI,SCI收录)

40.Heng Wang, Minping Jia, Peng Huang. A study on a new algorithm to optimize ball mill system based on modeling and GA[J]，Energy Conversion and Management, 51(4), pp 846-850,  2010.(EI,SCI收录)

41.陈蔚，贾民平，王恒. 基于信息融合的球磨机料位分级与检测研究[J]. 振动与冲击，2010,29（6）:140~143

42.王恒，贾民平，陈左亮. 基于LS-SVM和机理模型的球磨机料位软测量[J]. 电力自动化设备，2010，30（7）:92~95

43.王恒，贾民平，陈左亮. 球磨机负荷串级H_∞鲁棒控制器的设计与仿真[J]. 仪表技术与传感器，2010（11）:96~101