深度嵌入关系空间下齿轮箱标记样本扩充及其半监督故障诊断方法

Labeled sample augmentation based on deep embedding relation space for semi-supervised fault diagnosis of gearbox

针对只有少量标记样本的情况下,传统的基于深度学习的齿轮箱故障诊断方法训练出来的深度模型泛化能力差并且容易发生过拟合的问题,提出了一种基于深度嵌入关系空间下齿轮箱标记样本扩充的齿轮箱半监督故障诊断方法。该方法将少量的有标记振动信号以成对的输入方式输入到关系网络中进行监督训练,然后以有标记振动信号为参考,将大量的无标记振动信号输入到训练好的关系网络中,建立有标记信号与无标记信号的嵌入关系空间。在关系空间中将具有最大相似的无标记信号被挑选出,并赋予其预测标记作为伪标签添加到有标记振动信号集中,重复上述步骤以进行有标记样本集扩充,以提高关系网络的泛化能力,当关系网络训练好后用于机械故障诊断,实现故障的诊断及分类。实验结果表明:利用本诊断方法处理只有少量标记样本的齿轮振动信号时,成功地实现了少量标记样本的扩充,并取得了优于传统的监督和半监督故障诊断方法的齿轮箱故障辨识效果。

In the case of a small amount of labeled sample data, the deep model trained by the traditional deep learning-based gearbox fault diagnosis method has poor generalization ability, which is prone to over-fitting. To address this issue, a semi-supervised fault diagnosis method for the gearbox is proposed, which is based on the augmentation of labeled sample in deep embedding relation space. In this method, a small number of labeled vibration signals are input into the relation network in pairs for supervised training. Then, the labeled vibration signals are used as references, and a large number of unlabeled vibration signals are input into the trained relation network to establish the embedding relation space between labeled signals and unlabeled signals. In the relation space, some of the most similar signals are selected, and their predicted labels are set as pseudo labels and added to the labeled vibration signals. The above steps are iterated to expand the labeled samples to improve the generalization ability of the relation network. After the relation network is trained, it is used for mechanical fault diagnosis to realize fault diagnosis and classification. Experimental results show that the proposed method successfully expands the number of labeled sample when it is used to process the gear vibration signals with only a small number of labeled samples. The gearbox fault identification effect is better than the traditional supervised and semi-supervised fault diagnosis methods.