基于BP-AdaBoost的耦合碰摩故障特征识别研究
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摘要
旋转机械故障诊断研究中,采用BP神经网络容易陷入局部极小点而无法得到全局最优解,导致对耦合碰摩故障分类识别率不高的问题。研究了经验模态分解方法和BP-Ada Boost方法,结合二者优点,提出了一个故障识别的新方法,首先为了去除背景信号和噪声信号,选用经验模态分解方法来分解转子的振动信号,得到转子系统碰摩信号的主要故障特征,然后用BP-AdaBoost模型对3种不同工况进行识别。基于实验数据的分析表明方法的识别率要优于BP神经网络。
The BP neural network,used in the study of fault diagnosis for rotating machinery,is easy to fall into local minima,and then the global optimal solution could not be obtained,that results in a poor classification and recognition rate of couple rub impact fault.Based on the analysis of an Empirical Mode Decomposition(EMD) method and a BP-AdaBoost method,puts forward a new algorithm of fault identification combined with the advantages of them above.First of all,an Empirical Mode Decomposition(EMD) method is used to decompose vibration signal from the rotor,for the aim of removing background noise signal,so the fault feature of rub impact signal of rotor system is obtained.Then a BP-AdaBoost model is used for the recognition of three kinds of different conditions.The analysis results on the experiments data show that the recognition rate of this new method is better than that of a single BP neural network.
The BP neural network,used in the study of fault diagnosis for rotating machinery,is easy to fall into local minima,and then the global optimal solution could not be obtained,that results in a poor classification and recognition rate of couple rub impact fault.Based on the analysis of an Empirical Mode Decomposition(EMD) method and a BP-AdaBoost method,puts forward a new algorithm of fault identification combined with the advantages of them above.First of all,an Empirical Mode Decomposition(EMD) method is used to decompose vibration signal from the rotor,for the aim of removing background noise signal,so the fault feature of rub impact signal of rotor system is obtained.Then a BP-AdaBoost model is used for the recognition of three kinds of different conditions.The analysis results on the experiments data show that the recognition rate of this new method is better than that of a single BP neural network.
引文
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[2]崔慧敏.基于神经网络的旋转机械故障诊断方法[D].秦皇岛:燕山大学,2007.
[3]吕宣哲.基于小波变换和神经网络的旋转机械故障诊断研究[D].南京:南京航空航天大学,2012.
[4]冯志鹏,宋希庚,薛冬新.基于广义粗糙集与神经网络继承的旋转机械故障诊断研究[J].机械科学与技术,2003,22(5):815-820.
[5]王磊.基于HHT与神经网络的旋转机械故障诊断研究[D].南京:南京航空航天大学,2011.
[6]程军圣,于德介,杨宇.EMD方法在转子局部碰摩故障诊断中的应用[J].振动、测试与诊断,2006(1):24-27.
[7]程军圣,于德介,杨宇.基于EMD和分形维数的转子系统故障诊断[J].中国机械工程,2005(12):1088-1091.
[8]GAO Y L,GAO F.Edited Ada Boost by weighted k NN[Z].Neurocomputing,2010.
[9]史峰,王辉,郁磊,等.MATLAB智能算法30个案例分析[M].北京:北京航空航天大学出版社,2012:237-247.