多头注意力驱动的航空高速轴承故障诊断方法

A fault diagnosis method for aviation high-speed bearings driven by multi-head attention

航空发动机运行速度高、工况变化大、结构复杂且干扰噪声大,导致微弱故障特征往往存在于多子空间中,目前基于数据驱动的诊断模型尚不足以可靠捕捉不同子空间中丰富的特征信息。针对上述问题,提出一种基于信号特征的多头注意力诊断方法(multi-head attention diagnosis method,MADM),可实现高速非平稳工况下航空轴承故障状态的识别和诊断。该方法首先通过卷积模块和双向GRU模块对原始振动信号进行特征提取;然后引入多头注意力模块,使网络同时注意并融合不同表示子空间的信息以提高故障特征的显著性水平;最后利用全连接模块和Softmax分类器对提取的特征进行高速轴承故障诊断。试验结果表明,提出的MADM该诊断方法可实现转速为12000 r/min以上、剥落面积最小为0.5 mm^(2)的航空轴承高精度可靠诊断,且优于目前主流的深度诊断方法。

Due to the high running speed,large variation of operating conditions,complex structure and large interference noise,weak fault features often exist in multiple subspaces.At present,a data-driven diagnosis model is not enough to reliably capture rich feature information in different subspaces.In order to solve the above problems,a multi-head attention diagnosis method(MADM)based on signal features was proposed to identify and diagnose the fault state of aeronautical bearings under high-speed and non-stationary working conditions.In this method,the features of the original vibration signals were extracted by a convolution module and a bi-directional GRU module,and then the multi-head attention module was introduced to make the network pay attention to and fuse the information of different representation subspaces at the same time to improve the significance level of fault features.Finally,a full connection module and the Softmax classifier were used to diagnose high-speed bearing fault.Experimental results show that the proposed MADM diagnosis method can realize high precision and reliable diagnosis of aeronautical bearings with rotational speed of more than 12000 r/min and the minimum spalling area of 0.5 mm^(2),and is better than the current mainstream depth diagnosis methods.