方位(导航)
断层(地质)
模式识别(心理学)
主成分分析
计算机科学
堆积
希尔伯特-黄变换
算法
核主成分分析
人工智能
信号(编程语言)
非线性系统
核(代数)
支持向量机
数学
计算机视觉
核方法
地质学
物理
组合数学
滤波器(信号处理)
地震学
量子力学
核磁共振
程序设计语言
作者
Wenhe Chen,Longsheng Cheng,Zhipeng Chang,Liqun Fu
标识
DOI:10.1109/ictc51749.2021.9441600
摘要
Aiming at the nonlinear relationship between bearing fault signals, a fault diagnosis method based on KPCA and stacking algorithm is proposed to realize the common fault identification of rolling bearing. Firstly, Empirical Mode Decomposition (EMD) is conducted to decompose the bearing signal and extract the features to obtain the running state information of the bearing in different states. Then, Kernel Principal Component Analysis (KPCA) is applied to fuse features and reduce the dimension of bearing signals to reduce the influence of nonlinear correlation on fault identification. Finally, the stacking algorithm is used to identify the bearing fault signal, and the test data is used to validate it. The results show that the stacking algorithm based on KPCA can effectively identify the types of bearing fault.
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