In cloud manufacturing systems, fault diagnosis is essential for ensuring stable manufacturing processes. The most crucial performance indicators of fault diagnosis models are generalization and accuracy. An urgent problem is the lack and imbalance of fault data. To address this issue, in this article, most of existing approaches demand the label of faults as a priori knowledge and require extensive target fault data. These approaches may also ignore the heterogeneity of various equipment. We propose a cloud-edge collaborative method for adaptive fault diagnosis with label sampling space enlarging, named label-split multiple-inputs convolutional neural network, in cloud manufacturing. First, a multiattribute cooperative representation-based fault label sampling space enlarging approach is proposed to extend the variety of diagnosable faults. Besides, a multi-input multi-output data augmentation method with label-coupling weighted sampling is developed. In addition, a cloud-edge collaborative adaptation approach for fault diagnosis for scene-specific equipment in cloud manufacturing system is proposed. Experiments demonstrate the effectiveness and accuracy of our method.

中文翻译：

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LM-CNN：一种扩大标签采样空间的云边协作自适应故障诊断方法


在云制造系统中，故障诊断对于确保制造过程的稳定至关重要。故障诊断模型最关键的性能指标是泛化性和准确性。亟待解决的问题是故障数据的缺乏和不平衡。为了解决这个问题，在本文中，大多数现有方法都需要将故障标签作为先验知识，并且需要大量的目标故障数据。这些方法也可能忽略各种设备的异质性。我们提出了一种在云制造中通过扩大标签采样空间进行自适应故障诊断的云边协作方法，称为标签分割多输入卷积神经网络。首先，提出了一种基于多属性协作表示的故障标签采样空间扩大方法，以扩展可诊断故障的种类。此外，还开发了一种具有标签耦合加权采样的多输入多输出数据增强方法。此外，提出了云制造系统中场景特定设备故障诊断的云边协同自适应方法。实验证明了我们方法的有效性和准确性。