一种用于旋转机械故障特征提取的簇紧凑自编码器

针对传统自编码器以无监督方式学习特征、缺乏监督信息的指导造成特征判别性弱的问题,提出一种簇紧凑自编码器(cluster compact auto-encoder,CCAE).首先,利用模糊C均值算法对样本进行聚类得到伪标签,并通过PBMF指标确定最佳聚类数;然后,利用伪标签构建簇紧凑正则项,嵌入样本所属类别的判别性信息;最后,将簇紧凑正则项与标准自编码器的损失函数相结合作为CCAE的损失函数,所提出的CCAE通过伪标签的方式嵌入区分类别的判别性信息,可增强特征的判别性,从而显著提升诊断性能;最后,在旋转机械齿轮和轴承数据集上验证所提出方法的有效性,结果表明,CCAE可广泛用于旋转机械故障诊断的特征提取阶段,为工程人员实现判别性特征的自动提取提供一种解决方案.

A cluster compact auto-encoder for rotating machinery fault feature extraction

To deal with the problem that features learned by a traditional auto-encoder(AE) are less discriminative due to unsupervised manner, we propose a cluster compact auto-encoder(CCAE). First of all, a fuzzy C-means algorithm is used to cluster samples to get pseudo labels, where the optimal number of clusters is determined by the PBMF index. Then, a cluster compact regularization(CCR) is established based on the pseudo labels, which embeds discriminant information indicating categories of samples. Finally, the CCR is combined with the AE to constitute the CCAE''s loss function. Discriminant ability of the proposed method can be enhanced via the pseudo labels that incorporate discriminant information indicating categories, so as to improve the diagnostic performance greatly. The effectiveness of the proposed method is verified on rotating machinery gear and bearing datasets. The proposed CCAE can be widely applicable to the feature extraction stage of rotating machinery fault diagnosis, which provides a solution for engineers to realize automatic extraction of discriminative features.