噪声与振动控制2025，Vol.45Issue(5)：138-145,8.DOI:10.3969/j.issn.1006-1355.2025.05.022
基于智能频带分割的实时变速轴承故障诊断法
Real-time Bearing Fault Diagnosis under Variable-speed Condition Based on Intelligent Frequency Band Spliting
王思思 1蒋淑霞 1陈晓飞 1吴杰 1黄成祥1
作者信息
- 1. 中南林业科技大学 机械与智能制造学院,长沙 410004
- 折叠
摘要
Abstract
Aiming at the difficulty of feature extraction and online identification of rolling bearing fault signals under real-time variable-speed conditions,an innovative bearing fault diagnosis method is proposed.The method combines Frequency Band Spliting(FBS)and Genetic Algorithm-Backpropagation Neural Network(GA-BP).Firstly,wavelet packet energy spectrum is obtained by wavelet packet decomposition of the original signal.And then,the vibration signal and rotational speed signal mean and variance index are extracted to construct the characteristic parameter set,which is downsampled to further reduce the cost of data collection.Finally,the best hidden layer adaptive optimization system of GA-BP is used to realize the accurate recognition of fault features.The experiments and application cases show that the fault diagnosis accuracy of the bearing dataset of Case Western Reserve University in the United States can be up to 100%,and that of the bearing dataset of the University of Ottawa in Canada can be up to 99.4%,which fully proves the economic validity and good robustness characteristics of the proposed method.关键词
故障诊断/频带分割/降采样处理/实时变速工况/小波包能量谱Key words
fault diagnosis/FBS/downsampling processing/real-time variable speed conditions/wavelet packet energy spectrum分类
信息技术与安全科学引用本文复制引用
王思思,蒋淑霞,陈晓飞,吴杰,黄成祥..基于智能频带分割的实时变速轴承故障诊断法[J].噪声与振动控制,2025,45(5):138-145,8.
