A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Chiller fault detection and diagnosis with anomaly detective generative adversarial network
https://orcid.org/0000-0002-1611-6636
Abstract 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.
Highlights A chiller FDD framework with GAN for data augmentation. GANomaly algorithm is implemented to select the high-quality data samples. Comprehensive comparative study is conducted to show the effectiveness.
Chiller fault detection and diagnosis with anomaly detective generative adversarial network
https://orcid.org/0000-0002-1611-6636
Abstract 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.
Highlights A chiller FDD framework with GAN for data augmentation. GANomaly algorithm is implemented to select the high-quality data samples. Comprehensive comparative study is conducted to show the effectiveness.
Chiller fault detection and diagnosis with anomaly detective generative adversarial network
https://orcid.org/0000-0002-1611-6636
Abstract 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.
Highlights A chiller FDD framework with GAN for data augmentation. GANomaly algorithm is implemented to select the high-quality data samples. Comprehensive comparative study is conducted to show the effectiveness.
Chiller fault detection and diagnosis with anomaly detective generative adversarial network
Yan, Ke (author)
Building and Environment ; 201
2021-05-17
Article (Journal)
Electronic Resource
English
Generative adversarial network for fault detection diagnosis of chillers
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Application of Classification Functions to Chiller Fault Detection and Diagnosis
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Application of Classification Functions to Chiller Fault Detection and Diagnosis
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