基于交互式多头注意力的机械故障诊断方法

A FAULT DIAGNOSIS METHOD BASED ON INTERACTIVE MULTI-HEAD ATTENTION

针对旋转机械振动信号复杂且难以提取有效故障特征,多头注意力故障诊断方法计算复杂度高的情况,提出一种交互式多头注意力的机械故障诊断方法。通过对输入数据的特征图进行窗口分割,降低了注意力的计算复杂度。使用特征图滚动技术,在各自注意力之间和各窗口之间建立数据的联系,在降低计算复杂度的同时保证分类精度。将输入数据的位置信息融入注意力权重矩阵,增强神经网络对数据位置信息的辨别能力。在实验部分,为了测试算法的性能,将其应用到ZHS-2型多功能电机柔性转子试验台进行验证。实验结果表明,与其他数据驱动的故障诊断方法相比,该方法能更有效识别各种故障特征,实现故障诊断。

Aimed at the complex vibration signals of rotating machinery and the difficulty in extracting effective fault features and the problem that the multi head attention fault diagnosis method has high computational complexity,an interactive multi-head attention fault diagnosis method is proposed.By using the method of dividing the input data feature maps into windows,the computational complexity of attention was reduced.The rolled feature maps scheme was used to establish the data interaction between windows and the data interaction between self-attention,which reduced the computational complexity and ensured the classification accuracy.The position information of the input data was integrated into the attention weight matrix to enhance the ability of neural network to distinguish the position of input data.In the experiment part,in order to test the performance of the algorithm,it was applied to the ZHS-2 multi-function motor flexible rotor test bed to verify.Compared with other data-driven fault diagnosis methods,the results show that this method can effectively identify various fault features and realize fault diagnosis.