Zhang Xinliang, Wang Yanqi, Zhou Yitian, Jia Lijie
School of Electrical Engineering and Automation, Henan International Joint Laboratory of Direct Drive and Control of Intelligent Equipment, Henan Polytechnic University, Jiaozuo 454003, China.
Anhui Institute of Information Technology, Wuhu, Anhui 241000, China.
Rev Sci Instrum. 2024 May 1;95(5). doi: 10.1063/5.0193162.
Deep network fault diagnosis methods heavily rely on abundant labeled data for effective model training. However, small-sized samples and imbalanced samples often lead to insufficient features, resulting in accuracy degradation and even instability in the diagnosis model. To address this challenge, this paper introduces a coupled adversarial autoencoder (CoAAE) based on the Bayesian method. This model aims to solve the issue of insufficient samples by generating fake samples and integrating them with the original ones. Within the CoAAE framework, the probability density distribution of the original data is captured using an encoder and fake samples are generated by random sampling from this distribution and decoding them. This process is the adversarial interaction between the encoder and a classifier to obtain the prior distribution of the encoder's parameters. The encoder's parameters are updated through the decoder's reconstruction process, leading to the posterior distribution. Concurrently, the decoder is trained to enhance its ability to reconstruct samples accurately. To address the imbalance in the original samples, a parallel coupled network is employed. This network shares the weights of the extraction layer in the encoder, enabling it to learn the joint distribution between fault-related and normal samples. To evaluate the effectiveness of the proposed data augmentation method, experiments were conducted on a bearing database from Case Western Reserve University using ResNet18 as the deep learning diagnosis model representative. The results demonstrate that CoAAE can effectively augment imbalanced datasets and outperform other advanced methods.
深度网络故障诊断方法严重依赖大量有标签数据进行有效的模型训练。然而，小样本和不平衡样本往往导致特征不足，从而导致诊断模型的准确率下降甚至不稳定。为应对这一挑战，本文介绍了一种基于贝叶斯方法的耦合对抗自编码器（CoAAE）。该模型旨在通过生成虚假样本并将其与原始样本集成来解决样本不足的问题。在CoAAE框架内，使用编码器捕获原始数据的概率密度分布，并通过从该分布中随机采样并解码来生成虚假样本。这个过程是编码器与分类器之间的对抗交互，以获得编码器参数的先验分布。编码器的参数通过解码器的重建过程进行更新，从而得到后验分布。同时，训练解码器以增强其准确重建样本的能力。为解决原始样本中的不平衡问题，采用了一个并行耦合网络。该网络共享编码器中提取层的权重，使其能够学习故障相关样本和正常样本之间的联合分布。为评估所提出的数据增强方法的有效性，以凯斯西储大学的一个轴承数据库为实验对象，使用ResNet18作为深度学习诊断模型的代表进行了实验。结果表明，CoAAE可以有效地增强不平衡数据集，并且优于其他先进方法。
