粒子群优化
峰度
方位(导航)
计算机科学
模式识别(心理学)
人工神经网络
人工智能
特征向量
断层(地质)
熵(时间箭头)
小波
振动
特征(语言学)
特征提取
网络数据包
小波包分解
信号(编程语言)
支持向量机
小波变换
算法
数学
声学
语言学
统计
物理
哲学
量子力学
地震学
程序设计语言
地质学
计算机网络
作者
Yi Zhang,Jianfeng Qu,Xiaoyu Fang,Guojian Luo
标识
DOI:10.1109/scems52239.2021.9646168
摘要
This paper proposes fault a diagnosis method based on the combination of multi-feature fusion, particle swarm optimization and BP neural network(PSO-BP). In this method, the vibration signal of motor bearing is decomposed by wavelet packet, then the fuzzy entropy of each frequency band is solved, and finally the mixed feature vector is formed with the kurtosis of the original signal, which is input into PSO-BP for fault diagnosis. The simulation experiment is carried out using bearing data from CWRU, and the results prove that this method can more effectively distinguish the types of motor bearing faults.
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