It confronts great difficulty to apply traditional artificial intelligence (AI) techniques to machinery prognostics and health management in manufacturing systems due to the lack of abnormal samples corresponding to different fault conditions. This article explores an unsupervised feature learning method for machinery health monitoring by proposing a generative adversarial networks (GAN) model that exploits the merits of the autoencoder and the traditional GAN. The major contribution is that the data distribution of the normal samples is accurately learned by the GAN model within both the signal spectrum and latent representation spaces. Specifically, the discriminative feature for machinery health monitoring is learned in an unsupervised manner by the proposed method in three steps. First, the proposed GAN model is trained by the normal samples of the inspected machine with the aim to correctly reconstruct the signal spectrum and its latent representation. Then, the trained model is applied to test the online samples of the same machine with unknown health conditions. Finally, the dissimilarity between the tested samples and their reconstructed ones in the latent representation space is taken as the discriminative feature. The feature value will increase significantly if a fault occurs in the inspected machine because the abnormal samples are never trained in the proposed GAN model. Experimental studies on three different machines are conducted to validate the proposed method and its superiority over the traditional methods in detecting abnormal points and characterizing fault propagation.

中文翻译：

---

基于生成对抗网络的无监督特征学习的机械健康监测

由于缺乏对应于不同故障条件的异常样本，将传统的人工智能（AI）技术应用于制造系统中的机械预测和健康管理面临着巨大的困难。本文通过提出一种利用自动编码器和传统GAN优点的生成对抗网络（GAN）模型，探索了一种用于机器健康监控的无监督特征学习方法。主要贡献在于，GAN模型可以在信号频谱和潜在表示空间内准确地学习正常样本的数据分布。特别地，通过所提出的方法分三步以无监督的方式学习了用于机器健康监测的判别特征。第一，所提出的GAN模型是由被检查机器的正常样本训练的，目的是正确地重建信号频谱及其潜在表示。然后，将训练后的模型应用于测试具有未知健康状况的同一台机器的在线样本。最后，将测试样本与其在潜在表示空间中的重构样本之间的差异作为判别特征。如果在检查的机器中发生故障，则特征值将显着增加，因为在建议的GAN模型中从未训练过异常样本。在三种不同的机器上进行了实验研究，以验证该方法及其在检测异常点和表征故障传播方面优于传统方法的优越性。