一种BP神经网络的汽车齿轮箱故障诊断方法及实验验证
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摘要
在小波神经网络算法的基础上,从时域和频域两方面对汽车齿轮箱的振动信号进行分析并提取时频域的多个表征值,设计了一种应用于汽车齿轮箱故障诊断的BP神经网络算法。采用经验模态分解法对齿轮箱时频域下的多维故障特征值进行分析和提取,导出了BP神经网络算法步骤和诊断模型;进一步以JZQ! 250齿轮箱为研究对象,对该算法进行数据训练和验证,其状态实验数据结果表明,该算法能够在考虑汽车齿轮箱复杂故障下实现正确诊断,其用于汽车变速箱故障诊断具有较好的实用性,对汽车齿轮箱的故障诊断提供了一定借鉴。
Based on the wavelet neural network algorithm,the vibration signals of the automobile gearbox is analyzed from the time domain and the frequency domain,and the values with time domain and frequency domain characterization are extracted,and a method of BP neural network algorithm for the fault diagnosis of the automobile gearbox is designed. The fault of the gearbox is analyzed and extracted by empirical mode decomposition method in of the time-frequency domain. In addition,the method of BP neural network algorithm and the diagnosis models is erected. The JZQ-250 gearbox is used to train the algorithm of neural network algorithm to meet the needs of fault diagnose. The experimental results show that the BP neural network algorithm can reflect the correction of diagnosis under complicated fault of the automobile gearbox,which is of great practicality for fault diagnosis of the automobile gearbox. An idea and some experience for vehicle gearbox fault diagnosis in practice are provided.
Based on the wavelet neural network algorithm,the vibration signals of the automobile gearbox is analyzed from the time domain and the frequency domain,and the values with time domain and frequency domain characterization are extracted,and a method of BP neural network algorithm for the fault diagnosis of the automobile gearbox is designed. The fault of the gearbox is analyzed and extracted by empirical mode decomposition method in of the time-frequency domain. In addition,the method of BP neural network algorithm and the diagnosis models is erected. The JZQ-250 gearbox is used to train the algorithm of neural network algorithm to meet the needs of fault diagnose. The experimental results show that the BP neural network algorithm can reflect the correction of diagnosis under complicated fault of the automobile gearbox,which is of great practicality for fault diagnosis of the automobile gearbox. An idea and some experience for vehicle gearbox fault diagnosis in practice are provided.
引文
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[10]曹凤才,魏秀业,潘红侠.基于动态惯性权重粒子群算法的齿轮箱故障诊断研究[J].太原理工大学学报,2011(2):145-148.
[11] HUANG Zhonghua,XIE Ya. Fault diagnosis method of rolling bearing based on BP neural network[C]∥Proceedings of the 2009 International Conference on Measuring Technology and Mechatronics Automation,April 11-12,2009,Zhangjiajie,Hunan,China.New York:IEEE,c2009:647-649.
[12] FENG Z,CHEN X,LIANG M,et al. Time%frequency demodulation analysis based on iterative generalized demodulation for fault diagnosis of planetary gearbox under nonstationary conditions[J]. Mechanical Systems&Signal Processing,2015,62/63:54-74.
[13] LIU X,WANG D. Wavelet neural networks based fault diagnosis of analog circuit[C]∥Proceedings of the 24th Chinese Control and Decision Conference,May 23-25,2012,Taiyuan,Shanxi,China.New York:IEEE,c2012:2234-2239.
[2]申永军,杨绍普,刘献栋.齿轮故障诊断中的信号处理技术研究与展望[J].机械传动,2004,28(3):1-5.
[3]马彦斌,唐志生.基于时频特性和PCA-RBF神经网络的齿轮箱故障诊断[J].机电信息,2016(30):46-47.
[4]贺妍,马维金,刘迎各,等.基于时频特征提取与神经网络的齿轮箱故障诊断[J].机电信息,2016(12):68-69.
[5]古莹奎,孔军廷,朱繁泷.基于邻域属性重要度的齿轮箱故障特征优选方法[J].煤炭学报,2015(S2):560-567.
[6]古莹奎,杨子茜,朱繁泷.基于主成分分析的齿轮箱故障特征融合分析[J].中国机械工程,2015(11):1532-1537.
[7]方子帆,舒刚,何孔德,等.齿轮传动多体接触动力学模型[J].机械传动,2009,33(1):15-18.
[8]王江萍,孙文莉.基于小波包能量谱齿轮振动信号的分析与故障诊断[J].机械传动,2011,35(1):55-58.
[9]段志伟,高丙坤,王焕跃,等.基于LabVIEW的齿轮故障智能诊断系统[J].化工自动化及仪表,2015(1):77-81.
[10]曹凤才,魏秀业,潘红侠.基于动态惯性权重粒子群算法的齿轮箱故障诊断研究[J].太原理工大学学报,2011(2):145-148.
[11] HUANG Zhonghua,XIE Ya. Fault diagnosis method of rolling bearing based on BP neural network[C]∥Proceedings of the 2009 International Conference on Measuring Technology and Mechatronics Automation,April 11-12,2009,Zhangjiajie,Hunan,China.New York:IEEE,c2009:647-649.
[12] FENG Z,CHEN X,LIANG M,et al. Time%frequency demodulation analysis based on iterative generalized demodulation for fault diagnosis of planetary gearbox under nonstationary conditions[J]. Mechanical Systems&Signal Processing,2015,62/63:54-74.
[13] LIU X,WANG D. Wavelet neural networks based fault diagnosis of analog circuit[C]∥Proceedings of the 24th Chinese Control and Decision Conference,May 23-25,2012,Taiyuan,Shanxi,China.New York:IEEE,c2012:2234-2239.