机械与电子2026，Vol.44Issue(2)：53-61,9.
基于改进CAE-KPCA特征融合的轴承故障诊断
Fault Diagnosis of Bearings Based on Improved CAE-KPCA Feature Fusion
摘要
Abstract
To address the issues that traditional rolling bearing feature extraction methods have diffi-culty fully extracting fault pulses and exhibit limited feature extraction capabilities,this paper proposes a feature extraction method for rolling bearings based on improved CAE-KPCA framework.The original vi-bration signals are first converted into SDP images using the point symmetry analysis method.Abstract features are then extracted from these SDP images by an improved Convolutional Autoencoder(CAE),with the model trained efficiently using the Adam optimization algorithm for multi-parameter tuning.Subsequently,the histogram equalization is applied to process SDP images and calculate their statistical fea-ture set.A fault response gain index is used to screen for statistical features that demonstrate significant fault responses,so as to improve their quality.Finally,the Kernel Principal Component Analysis(KPCA)method is employed to reduce dimensionality and fuse the abstract features with the selected statistical fea-tures,eliminating redundancy among them.Experimental results show that compared to using either ab-stract features or statistical features alone,the fused features obtained by the improved CAE-KPCA meth-od achieve the highest fault diagnosis accuracy across several commonly used fault diagnosis models.关键词
滚动轴承/故障脉冲/特征融合/抽象特征/统计特征Key words
rolling bearing/fault impulse/feature fusion/abstract feature/statistical features分类
机械制造引用本文复制引用
马进,刘畅,陈文庆,胡雪年..基于改进CAE-KPCA特征融合的轴承故障诊断[J].机械与电子,2026,44(2):53-61,9.基金项目
国家自然科学基金青年基金项目(52304179) （52304179）
徐州市科技计划资助项目(KC22030) （KC22030）
