In most practical applications of fault diagnosis methods, two problems will inevitably arise. First, limited by the monitored object itself and its environment, accelerators are difficult to install. Second, industrial applications lack data with fault labels, which limits the use of data-driven-based methods. To solve these problems, a current signal-based adaptive semisupervised framework (C-ASSF) is proposed. In C-ASSF, the Wasserstein generative adversarial network with gradient penalty (WGAN-GP) is adopted to extract recognizable features from only normal current signals. Subsequently, since WGAN-GP pays too much attention to body signals and ignores the changes caused by faults, the line spectrum feature extraction (LSFE) technique is utilized to remove the main frequency component of the current signal specifically. Finally, an index indicating the degree of deviation from the normal distribution is introduced to identify external bearing faults in drivetrains. Two groups of different experimental data sets are applied to verify the performance of C-ASSF. The results show that C-ASSF is superior to existing methods, such as self-organizing map (SOM) and stack autoencoder (SAE), and can not only identify faults in drivetrains but also identify different fault classes.

中文翻译：

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用于传动系统轴承故障诊断的基于电流信号的自适应半监督框架

在故障诊断方法的大多数实际应用中，不可避免地会出现两个问题。首先，受监控对象本身及其环境的限制，加速器安装难度大。其次，工业应用缺乏带有故障标签的数据，这限制了基于数据驱动的方法的使用。为了解决这些问题，提出了当前基于信号的自适应半监督框架（C-ASSF）。在 C-ASSF 中，采用具有梯度惩罚的 Wasserstein 生成对抗网络（WGAN-GP）仅从正常电流信号中提取可识别的特征。随后，由于WGAN-GP过于关注车身信号而忽略了故障引起的变化，采用线谱特征提取（LSFE）技术专门去除当前信号的主频分量。最后，引入了指示偏离正态分布程度的指标，以识别传动系统中的外部轴承故障。应用两组不同的实验数据集来验证 C-ASSF 的性能。结果表明，C-ASSF 优于现有方法，如自组织映射 (SOM) 和堆栈自动编码器 (SAE)，不仅可以识别传动系统中的故障，还可以识别不同的故障类别。