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Chiller fault detection and diagnosis with anomaly detective generative adversarial network

机译：冷却器故障检测与诊断异常侦探生成对抗网络

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摘要

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 GANbased chiller FDD framework with GANomaly achieves the highest FDD accuracy than all compared methods.
机译：数据增强是冷却器自动数据驱动故障检测和诊断（FDD）过程中的必要步骤之一，而现实世界的操作培训样本通常是不平衡的。由于正常运行数据，故障的数据样本通常更难以收集。现有的作品表明，生成的对抗性网络（GAN）是有用的生成合成错误的数据样本，以丰富训练数据集。然而，自动FDD应用仍然是选择由GaN产生的高质量合成错误样本的问题。当不能完全控制合成故障数据样本的质量时，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的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，包括传统的分层自动编码器（VAE）和Ganomaly。通过一系列实验，有证明，通过少量的真实故障数据，拟议的GanAmased Chiller FDD框架与Ganomaly的FDD框架最高的FDD精度比所有比较方法都能实现最高的FDD精度。

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