一维深度子领域适配的不同转速下旋转机械 复合故障诊断

旋转机械复合故障与单一故障样本间相关性高易造成错分类,且旋转机械转速往往不同,进一步加剧了旋转机械复合故障诊断的难度。针对上述问题,提出一维深度子领域适配的不同转速下旋转机械复合故障诊断方法。首先,以旋转机械复合故障的频域信号作为网络的输入,最大程度保留信号特征;其次,搭建领域共享的一维卷积神经网络,对不同转速下旋转机械复合故障的频域信号特征进行学习;然后,添加局部最大均值差异形成子领域适配层,对齐每对子领域分布以避免单一故障和复合故障的特征混合,并通过最小化局部最大均值差异值缩小两域子领域特征分布差异,以减少不同转速所带来的干扰;最后,在子领域适配层后添加softmax分类层,实现对目标数据的故障状态识别。通过不同转速旋转机械复合故障诊断实验证明了所提方法的有效性。

Composite fault diagnosis of rotating machinery under different speed based on one dimensional deep subdomain adaption

The high correlation between single fault and composite fault samples, resulting in misclassification. Moreover, rotating machinery often works at different speeds, which further increases the difficulty of composite fault diagnosis of rotating machinery. Aiming at the above problems, a composite fault diagnosis method of rotating machinery at different speeds with one-dimensional depth subdomain adaptation was proposed. Firstly, frequency domain signals of composite faults of rotating machinery are used as the input of the network to get rid of the dependence on signal processing and professional knowledge; Secondly, a domain shared onedimensional convolutional neural network was built to learn the frequency domain signal characteristics of composite faults of rotating machinery at different speeds; Then, the local maximum mean difference is added to form the sub-domain adaptation layer, which aligns each pair of sub-domain distribution to avoid the feature mixing of single fault and compound fault, and reduces the feature distribution difference of the two subdomains by minimizing the local maximum mean difference to reduce the interference caused by different speeds. Finally, softmax classification layer is added after the sub-domain adaptation layer to realize fault state identification of the target data. The effectiveness of the proposed method is proved by the composite fault diagnosis experiments of rotating machinery at different speeds.