基于邻域属性重要度的齿轮箱故障特征优选方法
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摘要
为有效降低齿轮箱故障特征的维数并提高诊断效率,提出了基于邻域属性重要度的齿轮箱故障特征优选方法,并结合支持向量机和BP神经网络对诊断的准确率和时间进行对比分析。以齿轮箱中不同裂纹齿轮为对象,选取能够表征齿轮箱故障状态的时域、频域和基于希尔伯特变换的36个特征。构造基于邻域模型的前向贪心数值属性约简算法进行特征优选,提取属性重要度较大的9个特征组成最优特征子集,数据压缩量达到75%,并输入支持向量机分类器中进行分类识别,用BP神经网络分类器进行结果的比较分析。结果表明,采用基于邻域属性重要度的齿轮箱故障特征优选方法,既可以在降低特征维数的情况下有效地表征齿轮箱的运行状态,又可以提高诊断的精确度和诊断效率。
In order to reduce the dimension of gearbox fault feature and improve the efficiency of fault diagnosis,the optimal fault feature selection method of gearbox was proposed based on neighborhood attribute importance,and the support vector machine and BP neural network were used to analyze the diagnosis accuracy and time. The 36 features of the gears with different cracks in gearbox were selected based on the time-domain,frequency-domain and hilbert transform,which can be used to characterize the fault state of the gearbox. A forward-greedy numerical attribute reduction algorithm based on neighborhood model was established to select the optimal features. The nine features with higher attribute importance were selected as the optimal feature set and input into the support vector machine classifier for identification,where the data compression amount reach to 75%. The BP neural network classifier was used for a comparative analysis of the results. Results show that using the presented feature optimal selection method can reduce the feature dimension,characterize the gearbox running status effectively,and improve the diagnosis accuracy and efficiency.
In order to reduce the dimension of gearbox fault feature and improve the efficiency of fault diagnosis,the optimal fault feature selection method of gearbox was proposed based on neighborhood attribute importance,and the support vector machine and BP neural network were used to analyze the diagnosis accuracy and time. The 36 features of the gears with different cracks in gearbox were selected based on the time-domain,frequency-domain and hilbert transform,which can be used to characterize the fault state of the gearbox. A forward-greedy numerical attribute reduction algorithm based on neighborhood model was established to select the optimal features. The nine features with higher attribute importance were selected as the optimal feature set and input into the support vector machine classifier for identification,where the data compression amount reach to 75%. The BP neural network classifier was used for a comparative analysis of the results. Results show that using the presented feature optimal selection method can reduce the feature dimension,characterize the gearbox running status effectively,and improve the diagnosis accuracy and efficiency.
引文
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[13]Abu-mahfouz I A.A comparative study of three artificial neural networks for the detection and classification of gear faults[J].International Journal of General Systems,2005,34(3):261-277.
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[15]Lei Y G,He Z J,Zi Y Y,et al.Fault diagnosis of rotating machinery based on multiple ANFIS combination with GAs[J].Mechanical Systems and Signal Processing,2007,21(5):2280-2294.
[2]Zhao X M.Data-driven fault detection,isolation and identification of rotating machinery:with applications to pumps and gearboxes[D].Edmonton:University of Alberta,2012:5-8.
[3]李兵,张培林,任国全,等.基于互信息的滚动轴承故障特征选择方法[J].测试技术学报,2009,23(2):183-188.Li Bing,Zhang Peilin,Ren Guoquan,et al.Features selection for rolling bearing fault diagnosis based on mutual information[J].Journal of Test and Measurement Technology,2009,23(2):183-188.
[4]曾令男,丁建伟,赵炯,等.基于互信息的复杂装备高维状态监测数据相关性发现与建模[J].计算机集成制造系统,2013,19(12):3017-3025.Zeng Lingnan,Ding Jianwei,Zhao Jiong,et al.Detecting and modeling for associations between high-dimension condition monitoring data of complex equipment based on mutual information[J].Computer Integrated Manufacturing Systems,2013,19(12):3017-3025.
[5]Peng H C,Long F H,Ding C.Feature selection based on mutual information:criteria of max-dependency,max-relevance,and min-redundancy[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2005,27(8):1226-1238.
[6]王皓,孙宏斌,张伯明,等.基于混合互信息的特征选择方法及其在静态电压稳定评估中的应用[J].中国机电工程学报,2006,26(7):77-81.Wang Hao,Sun Hongbin,Zhang Boming,et al.Hybrid mutual information based feature selection method as appied to static voltage stability assessment in power systems[J].Proceedings of the CSEE,2006,26(7):77-81.
[7]刘晓平,郑海起,祝天宇.基于进化蒙特卡洛方法的特征选择在机械故障诊断中的应用[J].振动与冲击,2011,30(10):98-101.Liu Xiaoping,Zheng Haiqi,Zhu Tianyu.Feature selection in machine fault diagnosis based on evolutionary Monte Carlo method[J].Journal of Vibration and Shock,2011,30(10):98-101.
[8]Qu J.Support-vector-machine-based diagnostics and prognostics for rotating systems[D].Edmonton:University of Alberta,2011:9-12.
[9]蒋玲莉,刘义伦,李学军,等.基于SVM与多振动信息融合的齿轮故障诊断[J].中南大学学报(自然科学版),2010,41(6):2184-2188.Jiang Lingli,Liu Yilun,Li Xuejun,et al.Gear fault diagnosis based on SVM and multi-sensor information fusion[J].Journal of Central South University:Science and Technology,2010,41(6):2184-2188.
[10]聂磊,黄圣国,舒平,等.基于支持向量机(SVM)的民用飞机重着陆智能诊断研究[J].中国安全科学学报,2009,19(7):149-154.Nie Lei,Huang Shengguo,Shu Ping,et al.Intelligent diagnosis for hard landing of aircraft based on SVM[J].China Safety Science Journal,2009,19(7):149-154.
[11]丁世飞,齐丙娟,谭红艳.支持向量机理论与算法研究综述[J].电子科技大学学报,2011,40(1):2-10.Ding Shifei,Qi Bingjuan,Tan Hongyan.An overview on theory and algorithm of support vector machines[J].Journal of University of Electronic Science and Technology of China,2011,40(1):2-10.
[12]Lee M C.Using support vector machine with a hybird feature selection method to the stock trendprediction[J].Expert and Systems with Application,2009,36(8):10896-10904.
[13]Abu-mahfouz I A.A comparative study of three artificial neural networks for the detection and classification of gear faults[J].International Journal of General Systems,2005,34(3):261-277.
[14]胡清华,于任达,谢宗霞.基于邻域粒化和粗糙逼近的数值属性约简[J].软件学报,2008,19(3):640-649.Hu Qinghua,Yu Renda,Xie Zongxia.Numerical attribute reduction based on neighborhood granulation and rough approximation[J].Journal of Software,2008,19(3):640-649.
[15]Lei Y G,He Z J,Zi Y Y,et al.Fault diagnosis of rotating machinery based on multiple ANFIS combination with GAs[J].Mechanical Systems and Signal Processing,2007,21(5):2280-2294.