基于双重注意力机制及S-BiGAN的机电设备故障诊断

标签样本少条件下机电设备的准确故障诊断对于提高复杂机电设备的健康管理能力具有重要意义。针对标签样本少条件下难以建立准确故障诊断模型的问题，在半监督生成对抗网络的基础上，将注意力模块引入生成对抗网络，并利用格拉姆角场将一维数据转换为二维图像；结合双向生成对抗网络特点，提出一种基于双重注意力机制的半监督双向生成对抗网络（S-BIGAN）机电设备故障诊断模型，以轴承数据为例进行验证。结果表明：与CNN-SVM、SGAN 等算法相比，本文提出的模型能够提高样本生成质量和故障分类特征，有效解决标签样本少情况下的故障诊断问题，极大地提高了故障诊断准确率。

Mechanical and Electrical Equipment Fault Diagnosis Based on Dual Attention Mechanism and S-BiGAN

Accurate fault diagnosis of electromechanical equipment under the condition of limited label samples is of great significance for improving the health management ability of complex electromechanical equipment. This article proposes a semi supervised bidirectional generative adversarial network (S-BIGAN) based on dual attention mechanism for mechanical and electrical equipment fault diagnosis. GAF is used to convert one-dimensional data into two-dimensional images, effectively utilizing a small amount of labeled data and a large amount of unlabeled data. Finally, bearing data is used as the validation object, and compared with algorithms such as CNN SVM and SGAN, the accuracy of fault diagnosis is greatly improved.