基于混合特征和CFOA-GRNN的行星齿轮箱故障诊断研究  

Research on Fault Diagnosis of Planetary Gearbox Based on Hybrid Features and CFOA-GRNN

作　　者：许家才 吕亮 陆崇山 代劲 XU Jiacai;LYU Liang;LU Chongshan;DAI Jin(CHN Energy Yunnan New Energy Co.,Ltd.,Kunming Yunnan 650200,China;School of Power and Mechanical Engineering,Wuhan University,Wuhan Hubei 430072,China)

机构地区：[1]国能云南新能源有限公司,云南昆明650200 [2]武汉大学动力与机械学院,湖北武汉430072

出　　处：《机床与液压》2022年第6期186-191,共6页Machine Tool & Hydraulics

摘　　要：针对行星齿轮传动系统典型故障的识别,提出一种基于信号混合特征和混沌果蝇优化算法-广义回归神经网络(CFOA-GRNN)的故障诊断方法。计算信号的几种典型时域统计特征,并通过小波包分解获取信号频域能量特征,得到信号混合特征向量作为广义回归神经网络(GRNN)的输入;采用混沌扰动改进的果蝇优化算法对GRNN进行参数寻优,构建最优诊断模型;利用采集的行星齿轮箱实验台不同工况数据进行实验和对比。结果表明:所提方法能够有效识别不同工况下齿轮箱的不同故障;与其他模型相比,它具有参数设置简便、主观因素影响小、寻优速度快等优势,具有较好的实用性。Aiming at the identification of typical faults of planetary gear transmission systems,a fault diagnosis method based on signal mixing characteristics and chaos fruit fly optimization algorithm-generalized regression neural network(CFOA-GRNN)was proposed.Several typical time-domain statistical characteristics of the signal were calculated,the frequency-domain energy characteristics of the signal were obtained through wavelet packet decomposition,then the signal mixed characteristic vector was obtained as the input of the generalized regression neural network(GRNN);the improved fruit fly optimization algorithm with chaotic disturbance was used to find the optimal parameters of GRNN,and the optimal diagnosis model was constructed;the collected data under different working conditions of the gearbox test bench were used for application and comparison.The results show that the proposed method can be used to effectively identify different faults of the gearbox under different working conditions;compared with other models,the proposed method has the advantages of simple parameter setting,small subjective factors influence,fast optimization speed and better practicability.

关 键 词：行星齿轮箱 故障诊断 混合特征 混沌果蝇优化算法 广义回归神经网络 

分 类 号：TH132.41[机械工程—机械制造及自动化]