噪声与振动控制Issue(6)：169-173,5.DOI:10.3969/j.issn.1006-1335.2014.06.038
滚动轴承的MSE和PNN故障诊断方法
Fault Diagnosis of Rolling Bearings Using MSE and PNN
摘要
Abstract
Considering different levels of complexity of vibration signals of rolling bearings in different operating conditions, a novel fault diagnosis method has been proposed based on the multiscale entropy (MSE) and probabilistic neural networks (PNN). Fault feature vector is firstly extracted from the vibration signals using MSE and then provided to PNN neural network as the input. The PNN network will identify the bearing fault type and severity level simultaneously. The experimental data are collected from an induction motor bearing involving various fault types and severity levels. The results demonstrate that the proposed method has a higher accuracy in rolling bearing fault diagnosis than the method of the combination of wavelet packet decomposition with PNN.关键词
振动与波/多尺度熵/概率神经网络/滚动轴承/故障诊断Key words
vibration and wave/multiscale entropy/PNN/rolling bearing/fault diagnosis分类
机械制造引用本文复制引用
陈慧,张磊,熊国良,周继慧..滚动轴承的MSE和PNN故障诊断方法[J].噪声与振动控制,2014,(6):169-173,5.基金项目
国家自然科学基金资助项目（51205130、51265010）；江西省教育厅科技项目（GJJ12318）；江西省自然科学基金项目 （）
