Data augmentation is one of the necessary steps in the process of automated data-driven fault detection and diagnosis (FDD) for chillers, while real-world operational training samples are usually imbalanced. Faulty data samples are usually more difficult for collection than normal operation data. Existing works show that the generative adversarial networks (GAN) are useful generating synthetic faulty data samples to enrich the training dataset. However, it remains a problem for the automated FDD applications to select high-quality synthetic faulty samples generated by GAN. The FDD accuracy becomes unstable when the quality of synthetic fault data samples cannot be controlled entirely. In this study, we proposed to use the classic definition of anomaly detection to select high-quality synthetic fault data samples with the generative adversarial networks. Two anomaly detection methods were investigated, including the traditional variational auto-encoder (VAE) and the GANomaly. Through a series of experiments, it is justified that, with a small amount of real fault data, the proposed GAN-based chiller FDD framework with GANomaly achieves the highest FDD accuracy than all compared methods.

数据扩充是冷水机自动数据驱动型故障检测与诊断（FDD）过程中的必要步骤之一，而现实世界中的操作培训样本通常不平衡。故障数据样本通常比正常操作数据更难收集。现有工作表明，生成对抗网络 (GAN) 可用于生成合成错误数据样本以丰富训练数据集。然而，自动 FDD 应用程序选择由 GAN 生成的高质量合成错误样本仍然是一个问题。当合成故障数据样本的质量不能完全控制时，FDD 精度变得不稳定。在这项研究中，我们建议使用异常检测的经典定义来选择具有生成对抗网络的高质量合成故障数据样本。研究了两种异常检测方法，包括传统的变分自动编码器（VAE）和 GANomaly。通过一系列实验，证明在少量真实故障数据的情况下，所提出的基于 GAN 的冷水机组 FDD 框架与 GANomaly 相比所有比较方法实现了最高的 FDD 精度。