自编码
人工智能
计算机科学
分类器(UML)
稳健性(进化)
机器学习
训练集
深度学习
模式识别(心理学)
生物化学
基因
化学
作者
Ke Yan,Jianye Su,Jing Huang,Yuchang Mo
出处
期刊:IEEE Transactions on Automation Science and Engineering
[Institute of Electrical and Electronics Engineers]
日期:2022-01-01
卷期号:19 (1): 387-395
被引量:61
标识
DOI:10.1109/tase.2020.3035620
摘要
Artificial intelligence (AI)-enhanced automated fault diagnosis (AFD) has become increasingly popular for chiller fault diagnosis with promising classification performance. In practice, a sufficient number of fault samples are required by the AI methods in the training phase. However, faulty training samples are generally much more difficult to be collected than normal training samples. Data augmentation is introduced in these scenarios to enhance the training data set with synthetic data. In this study, a variational autoencoder-based conditional Wasserstein GAN with gradient penalty (CWGAN-GP-VAE) is proposed to diagnose various faults for chillers. A detailed comparative study has been conducted with real-world fault data samples to verify the effectiveness and robustness of the proposed methodology. Note to Practitioners—This work attacks the fact that faulty training samples are usually much harder to be collected than the normal training samples in the practice of chiller automated fault diagnosis (AFD). Modern supervised learning chiller AFD relies on a sufficient number of faulty training samples to train the classifier. When the number of faulty training samples is insufficient, the conventional AFD methods fail to work. This study proposed a variational autoencoder-based conditional Wasserstein GAN with gradient penalty (CWGAN-GP-VAE) framework for generating synthetic faulty training samples to enrich the training data set for machine learning-based AFD methods. The proposed algorithm has been carefully designed, implemented, and practically proved to be more effective than the existing methods in the literature.
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