基于流形嵌入分布对齐的滚动轴承迁移故障诊断方法

针对变工况下滚动轴承不易获取带标签的振动信号,导致故障诊断准确率低等问题,提出一种基于自适应噪声完整经验模态分解(CEEMDAN)与流形嵌入分布对齐的滚动轴承迁移故障诊断方法。采用CEEMDAN对不同工况下滚动轴承振动信号进行分解,得到若干内禀模态分量(IMF);提取峭度较大的IMF分量的时域和频域特征构造多特征样本集,将所提特征嵌入流形空间进行流形特征变换,同时,对变换后的流形特征动态分布对齐;利用源域数据和目标域数据训练分类模型,以获得未知标签的滚动轴承故障诊断结果。实验表明,所提方法能够最小化域间特征分布差异,有效提高滚动轴承状态识别的准确率。

Transfer fault diagnosis for rolling bearings based on manifold embedded distribution alignment

Vibration signals of rolling bearings with labels are difficult to obtain under variable working conditions,which leads to low accuracy of fault diagnosis.Aiming at this problem,a new fault diagnosis method for rolling bearings was proposed based on complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)and manifold embedding distribution alignment.Firstly,CEEMDAN was used to decompose the vibration signals of rolling bearings under different working conditions,and some intrinsic mode components(IMF)were obtained.Secondly,the time-domain and frequency-domain features of IMF components with larger kurtosis were extracted to construct a multi-features sample set.The extracted features were embedded into the manifold space for manifold feature transformation and the transformed manifold features were aligned dynamically.Finally,the classification model was trained with source data and target data to obtain the fault diagnosis results of rolling bearings with unknown labels.The experimental results show that the proposed method can minimize the difference of feature distribution between domains,and improve the accuracy of rolling bearings state recognition effectively.