降噪混合注意力变分自编码器及其在轴向柱塞泵故障诊断中的应用

Denoising Mixed Attention Variational Auto-encoder for Axial Piston Pump Fault Diagnosis

轴向柱塞泵作为液压系统的供能元件,其故障诊断研究具有重要意义。现有故障诊断方法大多依赖人工经验,提取故障特征需要大量专家知识,且对数据和噪声的鲁棒性较差。针对轴向柱塞泵服役环境复杂、监测信号易受噪声干扰的问题,提出端到端的降噪混合注意力变分自编码器方法,直接提取淹没在噪声中的故障特征,从而实现噪声环境下轴向柱塞泵故障诊断。该方法利用卷积变分自编码器从柱塞泵压力信号和振动信号中提取故障特征,通过引入混合注意力机制,实现编码器隐层特征的加权融合,以增强故障特征并削弱噪声影响;进一步使用自适应软阈值降噪方法,降低噪声干扰,完成强噪声环境下轴向柱塞泵故障诊断。通过轴向柱塞泵故障模拟试验和噪声鲁棒性试验对所提出方法的有效性进行验证,结果表明该方法在5dB噪声下的诊断准确率高达99.32%,在-5d B的强噪声影响下仍能保持69.72%的准确率,优于常用的故障诊断方法。

As an energy supply component of hydraulic system,the fault diagnosis of axial piston pump is of great significance.However,most existing methods rely on expert knowledge for feature extraction,and the robustness to noise is poor.To tackle the problem that complex working conditions bring noise interference to the collected diagnostic signals of axial piston pump,an end-to-end denoising mixed attention variational auto-encoder method is proposed to directly extract the fault characteristics submerged in the noise,to realize the fault diagnosis of axial piston pump under noisy environment.The proposed method employs convolution variational auto-encoder to extract fault features from multivariate signals including pressure and vibration.By introducing the mixed attention mechanism,hidden layer features of the encoder are weighted and fused,enhancing the fault features while weakening the noise.The adaptive soft-threshold denoising method is further applied to reducing the noise interference in extracted features,realizing the fault diagnosis of axial piston pump under strong noise.The effectiveness of the proposed method is verified by the fault implantation experiment and noise robustness experiment of an axial piston pump,and the results show 99.32%diagnosis accuracy under 5 dB noise and 69.72%under-5 dB noise,which outperforms commonly used diagnosis methods.