摘要
[目的]针对传统故障诊断方法无法充分发掘一维非线性时序振动信号所蕴含的故障信息、模型训练所需样本多和泛化性不足等问题,提出一种基于PSR-PCA-CNN的滚动轴承小样本智能故障诊断方法。[方法]首先,基于混沌理论的相空间重构(PSR)获取数据潜在的动力学特征,实现信号在相空间中的高维映射;然后,通过主成分分析(PCA)减轻数据维度冗余并产生简洁且富含信息的故障特征重构相图;最后,通过改进卷积神经网络(CNN)自动学习并提取复杂数据中的特征,实现小样本情况下滚动轴承的智能故障诊断。[结果]在两个轴承数据集上采用PSR-PCA-CNN方法进行故障诊断实验验证,准确率在97%以上;在训练集占比10%的小样本情况下训练准确率高于90%,与其他特征提取方法和先进算法相比,PSR-PCACNN方法具有更高的准确率。[结论]相较于其他图像编码方式与智能算法,PSR-PCA-CNN智能诊断方法在小样本情况下提取样本特征的能力优越,诊断效果良好,是解决滚动轴承小样本故障诊断任务的有效模型。
[Objective]A small sample intelligent fault diagnosis method for rolling bearings based on phase space reconstruction(PSR)combined with principal component analysis(PCA)and an improved convolutional neural network(CNN)is proposed to address the problems of traditional fault diagnosis methods being unable to fully explore the fault information contained in one-dimensional nonlinear temporal vibration signals,the large number of samples required for model training,and insufficient generalization.[Method]First,PSR based on chaos theory is used to recover the potential dynamic features of the data,achieving the high-dimensional mapping of signals in phase space.Next,PCA is used to reduce data dimensionality redundancy and generate concise and informative fault feature reconstruction phase diagrams.Finally,CNN is used to automatically learn and extract features from complex data,achieving the intelligent fault diagnosis of rolling bearings in small sample scenarios.[Results]The PSR-PCA-CNN method is validated using two bearing datasets,with experimental diagnostic accuracy exceeding 97%.In a small sample scenario with a 10%training set,the training accuracy is higher than 90%.Compared with other feature extraction methods and state-of-the-art algorithms,the PSR-PCA-CNN method has higher experimental accuracy.[Conclusion]Compared with other image encoding methods and intelligent algorithms,the intelligent diagnostic method proposed herein has good diagnostic performance and a superior ability to extract sample features in small sample scenarios,making it an effective model for solving the small sample fault diagnosis task of rolling bearings.
作者
管聪
吴超
张泽辉
范爱龙
徐晓滨
GUAN Cong;WU Chao;ZHANG Zehui;FAN Ailong;XU Xiaobin(School of Naval Architecture,Ocean and Energy Power Engineering,Wuhan University of Technology,Wuhan 430063,China;China-Austria Belt and Road Joint Laboratory on Artifical Intelligence and Advanced Manufacturing,Hangzhou Dianzi University,Hangzhou 310018,China;State Key Laboratory of Maritime Technology and Safety,Wuhan 430063,China)
出处
《中国舰船研究》
北大核心
2025年第S1期139-149,共11页
Chinese Journal of Ship Research
基金
浙江省自然科学基金(LTGG24F030004)
国家水运安全工程技术研究中心开放基金(A202403)
国家自然科学基金(52401376)
中央高校基本科研业务费专项资金(104972024JYS0043)。
关键词
轴承
滚动轴承
人工智能
故障诊断
预防性维修
相空间重构
主成分分析
卷积神经网络
bearings(machine parts)
rolling bearings
artificial intelligence
fault diagnosis
preventive maintenance
phase space reconstruction
principal component analysis
convolutional neural network
