基于小波包Shannon熵与GA-SVM的船舶轴系故障诊断方法
PDF下载 (320)汪 明,胡海刚 *,张 刚,周 昕.基于小波包Shannon熵与GA-SVM的船舶轴系故障诊断方法[J].宁波大学学报(理工版),2015,28(04):124-128.DOI:
WANG Ming,HU Hai-gang *,ZHANG Gang,ZHOU Xin.Fault Diagnosis Methods of Ship Shafting Based on Wavelet Packet, Shannon Entropy, GA and SVM[J].Journal of Ningbo University(Natural Science & Engineering Edition),2015,28(04):124-128.DOI:
| Title: | Fault Diagnosis Methods of Ship Shafting Based on Wavelet Packet, Shannon Entropy, GA and SVM |
| 作者: | 汪 明, 胡海刚 *, 张 刚, 周 昕 |
| Author(s): | WANG Ming, HU Hai-gang *, ZHANG Gang, ZHOU Xin |
| 关键词: | 船舶轴系; 故障诊断; 遗传算法; 支持向量机 |
| Keywords: | ship shafting; fault diagnosis; genetic algorithm; support vector machine |
| 分类号: | U664 |
| 文献标识码: | A |
| 摘要: | 针对船舶推进轴系的振动问题, 基于小波包、Shannon熵、遗传算法(GA)和支持向量机(SVM)理论, 提出了一种船舶轴系故障诊断的新方法, 简称WPS-GS方法. 该方法依托船舶螺旋桨状态监测模拟实验平台, 利用小波包分解技术分析船舶轴系发生故障时的振动信号, 将其Shannon熵作为SVM的输入特征向量. 在训练SVM时, 采用遗传算法对SVM的参数进行全局寻优, 使SVM具有更高的识别准确率. 实验结果表明, WPS-GS方法对故障诊断的准确度和识别率较传统SVM和交叉验证SVM方法高, 适用于船舶轴系故障诊断. |
| Abstract: | Aiming at the vibration problems of the ship propulsion shafting, a new method of fault diagnosis, which is based on the theory of wavelet packet (wavelet packet), Shannon entropy, genetic algorithm (GA) and support vector machine (SVM), is proposed, and referred to as WPS-GS method in this paper. For the simulation of platform ship propeller shafting, the wavelet packet decomposition and strong fault-tolerant Shannon entropy are jointly used to compute the feature vectors of vibration signals which are served as the input vectors of SVM; GA is adopted to optimize the parameters of SVM for achieving the higher veracity. The simulation results show that the WPS-GS method can attain higher reliability and veracity than the conventional SVM and K-CV SVM, which suggests that the proposed method is more suitable for the condition monitoring and fault diagnosis of rotating shaft system. |
| 参考文献 /References: | [1] 史晓敏. 船舶螺旋桨的偏振和抗冲击稳定性研究[J]. 机电工程, 2007, 24(9):17-18. [2] 尚二国. 调距桨推进装置控制系统的研究与设计[D].武汉: 武汉理工大学, 2009. [3] 孟昭玉. 船舶螺旋桨轴状态检测技术[D]. 上海: 上海海事大学, 2005. [4] 黄辉, 褚建新, 沈爱弟. 基于小波分析的船舶螺旋桨故障诊断[J]. 船舶工程, 2014, 36(1):45-48. [5] Wu J D, Liu C H. An expert system for fault diagnosis in internal combustion engines using wavelet packet transform and neural network[J]. Expert Systems with Applications, 2009, 36(3):4278-4286. [6] Kimh C, Pang S, Hm J, et al. Constructing support vector machine ensemble[J]. Pattern Recognition, 2003, 36(12): 2757-2767. [7] PLATT J C. Fast training of support vector machines using sequential minimal optimization[C]//Scholkopf B. Advance in Kernel Methods Support Vector Learning, Cambrifge Massachusetts: MIT Press, 1999:185-208. [8] 周昕, 凌兴宏. 遗传算法理论及技术研究综述[J]. 计算机与信息技术, 2010, 4:37-39. [9] 胡海刚, 冯志敏, 蒲龙云. 基于虚拟仪器的扭振检测仪设计[J]. 机床与液压, 2003, 6:289-291. [10] 张泉南, 于尧治. 特征分析及其在船舶轴系故障诊断中的应用[J]. 振动与冲击, 1986(2):62-70. [11] 周春良. 船舶轴系振动研究[D]. 哈尔滨: 哈尔滨工程大学, 2006. |
| 备注/Memo: | 收稿日期: 2014-10-17. 宁波大学学报（理工版）网址: http://journallg.nbu.edu.cn/ 基金项目: 浙江省科技厅公益项目（2013C31045）; 宁波大学实验技术项目（SYJS-201406）. 第一作者: 汪明（1988－）, 男, 湖北随州人, 在读硕士研究生, 主要研究方向: 结构振动监测与控制研究. E-mail: nbdxhyxywm@163.com *通信作者: 胡海刚（1966－）, 男, 浙江舟山人, 高级实验师, 主要研究方向: 船舶工程安全技术与机电控制. E-mail: huhaigang@nbu.edu.cn 宁波大学学报（理工版）网址：http://journallg.nbu.edu.cn/ |
