摘要
针对滚动轴承振动信号复杂及故障类型难以预知的问题,提出一种基于模拟退火粒子群算法(SAPSO)优化小波神经网络来诊断滚动轴承故障的新方法,并将其应用于滚动轴承故障诊断。实验表明,该方法能减少迭代次数、提高收敛精度。
According to the questions, the vibration signal is complex and the iault mode foreknew as difficult a novel method of unknown exception diagnosis in rolling bearing based on the wavelet neural networks （WNN）is proposed based on particle swarm optimiza-tion based on simulated annealing （SAPSO） is proposed to optimize parameters of WNN. As this new algorithm has some virtues such as high convergence speed and not easily trapping local mini-ma. Then,the algorithm is applied to neural network＇s training in fault diagnosis of rolling bearing.The experimental result indicates that the algorithm can reduce the number of training and error.
出处
《煤矿机械》
北大核心
2009年第10期228-231,共4页
Coal Mine Machinery
关键词
滚动轴承
故障诊断
小波神经网络
rolling bearing
fault diagnosis
wavelet neural networks
