摘要
为了防止滚动轴承早期微弱故障演变为严重故障,本文提出了一种新的故障检测方法。首先采用PCA(主成分分析)对振动信号进行特征筛选,降低数据维度,简化振动数据的结构,增强特征的表达力。然后,使用CEEMDAN(完全自适应噪声集合经验模态分解)算法来分解被背景噪声干扰的微弱故障振动信号,在经验模态分解(EMD)的基础上引入自适应噪声,增强对微弱特征的识别能力,分离出趋势和噪声数据,以提高故障诊断的准确性。最后,引入Transformer模型,进一步优化特征的提取和表征,实现对长序列数据的高效处理,用于微弱故障特征的提取和表征。这一综合方法具有降维、噪声抑制和长序列处理等多重优势,有望在滚动轴承故障检测中取得显著成果。
To prevent early weak faults in rolling bearings from evolving into severe faults,this paper proposes a new fault detection method.Initially,principal component analysis(PCA)is employed for feature selection of vibration signals to reduce data dimensions,simplify the structure of vibration data,and enhance feature expressiveness.Then,the complete ensemble empirical mode decomposition with adaptive noise algorithm(CEEMDAN)is used to decompose weak fault vibration signals interfered by background noise.By introducing adaptive noise on top of empirical mode decomposition(EMD),this method enhances the recognition of weak features,separating trend and noise data to improve the accuracy of fault diagnosis.Finally,the transformer model is incorporated to further optimize feature extraction and representation,achieving efficient processing of long sequence data for weak fault feature extraction and characterization.This comprehensive approach,with its advantages in dimension reduction,noise suppression,and long sequence processing,holds promise for significant achievements in fault detection of rolling bearings.
作者
段彩丽
马驰
张建生
呼志广
张宝宏
冯瑞
王飞
DUAN Caili;MA Chi;ZHANG Jiansheng;HU Zhiguang;ZHANG Baohong;FENG Rui;WANG Fei(National Energy Group Guoshen Technology Research Institute,Xi'an 710065,Shaanxi,China;Guoyuan Electric Power Co.,Ltd.,National Energy Group,Beijing 100033,China;Guoneng Power ShanXi Hequ Power Generation Co.,Ltd.,Xinzhou 036500,Shanxi,China;Guoneng Power HaMi Dananhu Power,Hami 839000,Xinjiang,China.)
出处
《电力大数据》
2023年第9期40-48,共9页
Power Systems and Big Data
基金
国网江苏电力有限公司科技项目(J2020120)。
