HHT和SVM在机械安全评估与预测中的应用研究

doi:10.16265/j.cnki.issn1003-3033.2017.02.011

中国安全科学学报 ›› 2017, Vol. 27 ›› Issue (2): 58-63.doi: 10.16265/j.cnki.issn1003-3033.2017.02.011

HHT和SVM在机械安全评估与预测中的应用研究

宣金泉, 王晓红副教授, 陆大伟, 王立志

北京航空航天大学可靠性与系统工程学院, 北京 100191

收稿日期:2016-10-19 修回日期:2016-12-24 出版日期:2017-02-28 发布日期:2020-11-22
通讯作者: 王立志(1985—),男,黑龙江哈尔滨人,博士,讲师,研究生导师,主要从事可靠性试验与评估,多源信息融合技术等方面的研究。E-mail:wanglizhi@buaa.edu.cn。
作者简介:宣金泉 (1991—),男,安徽肥东人,硕士研究生,研究方向为可靠性与环境试验技术、故障与寿命预测、安全分析与评估技术。E-mail: xjq304@163.com。
基金资助:
中央高校基本科研业务费专项资金(2014ZC51031);航空科学基金资助(2015ZD51044)。

Research on application of HHT and SVM in safety assessment and prediction for mechanical equipment

XUAN Jinquan, WANG Xiaohong, LU Dawei, WANG Lizhi

School of Reliability and System Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100191, China

Received:2016-10-19 Revised:2016-12-24 Online:2017-02-28 Published:2020-11-22

摘要/Abstract

摘要： 为提高大型复杂机械设备运行的安全性和可靠性,在监测机械设备振动状态的基础上,采用希尔伯特-黄变换(HHT)技术处理信号,将获得的振动频域能量值作为机械设备性能退化的特征量;进而采用网格搜索法(GS)和交叉验证法(CV),优化支持向量机模型(SVM)参数,以提高退化特征量预测精度;并据此建立一种状态空间划分法,用以评估并预测机械设备安全状态。最后,用所建立的方法评估并预测无刷直流电机振动状态和相应的安全状态,预测结果的相对误差仅为1.17%。

关键词: 机械设备, 振动监测, 希尔伯特-黄变换(HHT), 支持向量机(SVM), 安全评估与预测

Abstract: In this paper, the vibration condition monitoring technique is adopted for mechanical equipment, based on which the HHT method is utilized to processvibration signals; the vibration frequency-domain energy value obtained is taken as the characteristic quantity to represent the performance degradation of mechanical equipment. Then the Grid Search(GS) and Cross Validation(CV) methods are used to optimize the parameters of SVM, so as to improve the prediction accuracy of degradation characteristic quantity. Therefore, a state space division method is developed to assess and predict the safety status of mechanical equipment. Finally, the method developed by the authors was used for assessing and predicting the vibration state and the corresponding safety status of brushless direct current motors.The results show that the prediction error is only 1.17%.

Key words: mechanical equipment, vibration monitoring, Hilbert-Huang transform(HHT), support vector machine(SVM), safety assessment and prediction

中图分类号:

宣金泉, 王晓红, 陆大伟, 王立志. HHT和SVM在机械安全评估与预测中的应用研究[J]. 中国安全科学学报, 2017, 27(2): 58-63.

XUAN Jinquan, WANG Xiaohong, LU Dawei, WANG Lizhi. Research on application of HHT and SVM in safety assessment and prediction for mechanical equipment[J]. China Safety Science Journal, 2017, 27(2): 58-63.

参考文献

[1] 王福绵. 2006—2011年起重机械事故统计分析与防范[J].中国特种设备安全, 2013, 29(2):49-51.
WANG Fumian. Accident analysis and prevention statistics of Lifting machinery from 2006 to 2011[J].China Special Equipment Safety, 2013, 29(2):49-51.
[2] ROVERI N, CARCATERRA A. Damage detection in structures under traveling loads by Hilbert-Huang transform[J].Mechanical Systems and Signal Processing, 2012, 28(4):128-144.
[3] 刘俊,王占林,付永领,等. 基于改进HHT的一体化电液作动器故障诊断[J].北京航空航天大学学报, 2013, 39(1):62-66.
LIU Jun, WANG Zhanlin, FU Yongling, et al. Fault diagnosis of EHA-VPVM based on improved HHT[J].Journal of Beijing University of Aeronautics and Astronautics, 2013, 39(1):62-66.
[4] SOUALHIAbdenour, MEDJAHERKKamal, ZERHOUNINNoureddine .Bearing health monitoring based on Hilbert-Huang transform, support vector machine, and regression[J].IEEE Transactions on Instrumentation and Measurement, 2015,64(1):52-62.
[5] CARLOS J, CECILIO BV. Support vector machines used to estimate the battery state of charge[J].IEEE Transactions on Power Electronics, 2013, 28(12):5 919-5 926.
[6] 丛林虎,徐廷学,王骞,等. 基于退化数据与故障数据的导弹竞争故障预测[J].北京航空航天大学学报, 2016, 42(3):522-531.
CONG Linhu, XU Tingxue, WANG Qian, et al. Missile competing fault prediction based on degradation data and fault data[J].Journal of Beijing University of Aeronautics and Astronautics, 2016, 42(3):522-531.
[7] BENKEDJOUH T, MEDJAHER K, ZERHOUNI N et al. Health assessment and life prediction of cutting tools based on support vector regression[J].Journal of Intelligent Manufacturing, 2015, 26(2):213-223.
[8] ZHAO Yan, LIU Enshu, ZHU Junchao et al. Rub-impact fault diagnosis of rotating machinery based on Hilbert-Huang transform[C]. IEEE International Conference on Mechatronics and Automation (ICMA). Beijing: IEEE Press, 2015:32-36.
[9] 田丰, 李钫.基于HHT的局部放电在线监测与电缆安全的相关性研究[J].中国安全科学学报,2013, 23(7):38-42.
TIAN Feng, LI Fang. Study on online detection of local discharge and cable safety based on HHT[J]. China Safety Science Journal,2013, 23(7):38-42.
[10] 董丁稳, 李树刚, 常心坦,等. 基于 HHT 方法的矿井瓦斯体积分数预测[J].中国安全科学学报,2011,21(9):100-105.
DONG Dingwen,LI Shugang,CHANG Xintan, et al. Mine gas concentration prediction based on HHT method[J]. China Safety Science Journal, 2011,21(9):100-105.
[11] 王立,于重重,施彦,等.基于振动信号分析的电机性能退化特征提取[J].计算机仿真, 2014, 31(4): 416-421.
WANG Li, YU Chongchong, SHI Yan,et al. Degradation characteristics extraction of electric motor properties based on vibration signal analysis[J].Computer Simulation, 2014, 31(4): 416-421.
[12] JIANG Annan, WANG Shanyong, TANG Shulin. Feedback analysis of tunnel construction using a hybrid arithmetic based on support vector machine and particle swarm optimization[J]. Automation in Construction, 2011, 20(4):482-489.
[13] KONAR P, CHATTOPADHYAY P. Bearing fault detection of induction motor using wavelet and support vector machines (SVMs)[J]. Applied Soft Computing, 2011, 11(6): 4 203-4 211.
[14] VANTUNG T, PHAM H T, YANG B S, et al. Machine performance degradation assessment and remaining useful life prediction using proportional hazard model and support vector machine[J]. Mechanical Systems and Signal Processing, 2012, 32(11):320-330.

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HHT和SVM在机械安全评估与预测中的应用研究

Research on application of HHT and SVM in safety assessment and prediction for mechanical equipment

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