基于小样本学习的滚动轴承故障检测

Rolling Bearing Fault Detection Based on Few-Shot Learning

轴承故障类型复杂,并且在不同工况下每种故障类型都很难获得足够的训练样本。因此,本文提出一种基于深度神经网络的小样本学习分类算法,引入第1层具有宽卷积核网络(Convolutional neural network with training interference,TICNN)作为孪生网络的子网络用于提取特征,减少工业环境噪声影响。孪生网络是一种常用于小样本学习的结构,通过输入相同或不同类别的样本对进行训练,学习不同属性样本与特征之间的映射关系,并采用相似度进行度量。测试样本通过寻找最近邻的类别来实现分类。在标准凯斯西储大学轴承故障诊断基准数据集上的实验结果表明,在数据有限的情况下,本文模型在故障诊断中表现出更好的效果。当使用最少的训练数据在不同的噪声环境中进行测试时,本文小样本学习模型的性能超过了具有合理噪声水平的基线模型,故障诊断准确率达到了94.41%。当在具有新故障类型或新工作条件的测试集上进行评估时,本文模型仍然有效。

Bearing fault types are complex,and it is difficult to obtain enough training samples for each fault type under different working conditions.Convolutional neural network with training interference(TICNN)with wide convolutional kernel is introduced as the subnetwork of the Siamese network used to extract features,reducing the impact of industrial environment noise.Siamese network is a structure commonly used for few-shot learning.By inputting the same or different categories of samples for training,the mapping relationship between different attribute samples and features is learned,and the similarity between samples is used as measure index.The test sample is classified by finding the class of the nearest neighbor.Experimental results on the standard Case Western Reserve University(CWRU)bearing fault diagnosis benchmark dataset show that,in the case of limited data,the proposed model shows better results in fault diagnosis.The performance of the proposed few shot learning model exceeds the baseline model with a reasonable noise level when testing with the least training data in different noise environments,and the accuracy of fault diagnosis reaches 94.41%.When evaluating on test sets with new fault types or new working conditions,the proposed model also performs well.