基于小波和Bagging-PNN网络的柴油机轴承故障研究
A Study of Diesel Engine Bearing Failure Based on Wavelet and Bagging-PNN Networks
丁坤岭 1王晓峰 2舒航 1徐可 2孙贾梦1
作者信息
- 1. 重庆科技大学 电子与电气工程学院,重庆 401331
- 2. 重庆科技大学 数理科学学院,重庆 401331
- 折叠
摘要
针对柴油机故障诊断速度慢、诊断模型准确率低等问题.提出一种基于小波和Bagging-PNN网络的柴油机轴承故障诊断法.首先,利用时域、频域对采样后的故障数据进行分析,通过小波分析对数据进行去噪处理;然后,将Bagging算法与概率神经网络(Probabilistic Neural Network,PNN)进行融合,通过多个PNN分类器以相同的方式进行投票建立柴油机轴承故障分类模型,提高诊断准确度;最后,通过对比实验表明基于小波和Bagging-PNN的柴油机轴承故障诊断方法的识别准确性有明显提高.
Abstract
Aiming at the problems of slow speed and low accuracy of diagnostic model for diesel engine fault,a diesel engine bearing fault diagnosis method based on wavelet and Bagging-PNN network is proposed.First,the sampled fault data are analyzed in time and frequency domains,and the data are denoised by wavelet analysis;then,the Bagging algorithm is fused with Probabilistic Neural Network(PNN),and the data obtained by multiple PNN classifiers voting in the same way are used as the final classification results.The output of the denoised data is used to establish a diesel engine bearing fault classification model to improve the diagnostic ac-curacy;finally,the comparison experiments show that the recognition accuracy of the wavelet and Bagging-PNN based diesel engine bearing fault diagnosis method is significantly improved.
关键词
柴油机轴承/故障诊断/Bagging/PNN/小波分析Key words
diesel engine bearings/fault diagnosis/Bagging/PNN/wavelet analysis引用本文复制引用
出版年
2024
NETL
NSTL
万方数据