一种基于Triplet loss的齿轮箱复合故障识别方法

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振动与冲击 ›› 2021, Vol. 40 ›› Issue (5) : 46-54.

论文

赵晓平1,3，王逸飞 2，张永宏 2，吴家新 1，王丽华 2

作者信息 +

A compound fault identification method for gearbox based on Triplet loss

ZHAO Xiaoping1，3, WANG Yifei2, ZHANG Yonghong2, WU Jiaxin1, WANG Lihua2

Author information +

文章历史 +

摘要

随着设备检测点的数量与采样频率的增加，机械健康监测进入了“大数据”时代。深度学习以其强大的自适应特征提取和分类能力也在机械大数据处理方面取得了丰硕的成果。在故障诊断领域，目前深度学习方法的研究对象均集中于单一故障，而复合故障却鲜有人涉足。复合故障因为其各类故障信号间有耦合，变化的工况（负载，转速）也会对信号产生较大影响，所以难以准确诊断。面对复杂的复合故障，传统的Softmax分类器已不能精确高效的完成故障诊断。本文提出了一种基于Triplet loss的深度度量学习模型的诊断方法，对齿轮箱的轴承及齿轮这两种目标的故障同时进行诊断。其优势在于通过该模型提取故障信号的特征，再利用Triplet loss度量各类故障之间的距离，使得同类故障特征间的距离很近，异类故障特征间的距离很远，从而高效完成诊断任务。实验结果表明，该方法实现了在多种工况，大量样本下对齿轮箱内轴承和齿轮不同故障的准确诊断。

Abstract

With the increase in the number of device detection points and the sampling frequency, mechanical health monitoring has entered the era of big data. With its powerful adaptive feature extraction and classification capabilities, deep learning has achieved good results in mechanical data processing. However, most research objects in the field of fault diagnosis are single faults. Multi-fault is difficult to diagnose accurately because of the coupling between various kinds of fault signals and the great influence of changing working conditions on the signals. in the case of a wide variety of composite faults, the traditional softmax classifier cannot complete fault diagnosis accurately and efficiently. To solve this problem, this paper proposes deep metric learning based on triplet loss to complete the diagnostic classification of bearing and gear of gearbox simultaneously. The advantage of this model is to extract the features of fault signals, and then measure the distances between different kinds of faults by Triplet loss, which makes the distances between similar fault features very close, and between different fault features very far, thus completing the diagnosis task efficiently. The experimental results show that the method can accurately diagnose the different faults of the inner bearing and the gear under various working conditions and a large number of samples.

导出引用

赵晓平1,3，王逸飞 2，张永宏 2，吴家新 1，王丽华 2. 一种基于Triplet loss的齿轮箱复合故障识别方法[J]. 振动与冲击, 2021, 40(5): 46-54

ZHAO Xiaoping1，3, WANG Yifei2, ZHANG Yonghong2, WU Jiaxin1, WANG Lihua2. A compound fault identification method for gearbox based on Triplet loss[J]. Journal of Vibration and Shock, 2021, 40(5): 46-54

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