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A platform for research: civil engineering, architecture and urbanism
Unsupervised learning for fault detection and diagnosis of air handling units
Highlights This work applies an unsupervised learning technique, generative adversarial network (GAN), to deal with the imbalanced training dataset problem in data-driven AHU FDD. Detailed steps of applying GAN to generate artificial faulty training samples are demonstrated and deeply analyzed. A complete AHU FDD framework integrating extensions of GAN is proposed. The existing GAN-based AHU FDD framework has been optimized. Comprehensive experimental results are shown before and after applying GAN extensions to the traditional supervised FDD framework to illustrate the importance of GAN.
Abstract Supervised learning techniques have witnessed significant successes in fault detection and diagnosis (FDD) for heating ventilation and air-conditioning (HVAC) systems. Despite the good performance, these techniques heavily rely on balanced datasets that contain a large amount of both faulty and normal data points. In real-world scenarios, however, it is often very challenging to collect a sufficient amount of faulty training samples that are necessary for building a balanced training dataset. In this paper, we introduce a framework that utilizes the generative adversarial network (GAN) to address the imbalanced data problem in FDD for air handling units (AHUs). To this end, we first show the necessary procedures of applying GAN to increase the number of faulty training samples in the training pool and re-balance the training dataset. The proposed framework then uses supervised classifiers to train the re-balanced datasets. Finally, we present a comparative study that illustrates the advantages of the proposed method for FDD of AHU with various evaluation metrics. Our work demonstrates the promising prospects of performing robust FDD of AHU with a limited number of faulty training samples.
Unsupervised learning for fault detection and diagnosis of air handling units
Highlights This work applies an unsupervised learning technique, generative adversarial network (GAN), to deal with the imbalanced training dataset problem in data-driven AHU FDD. Detailed steps of applying GAN to generate artificial faulty training samples are demonstrated and deeply analyzed. A complete AHU FDD framework integrating extensions of GAN is proposed. The existing GAN-based AHU FDD framework has been optimized. Comprehensive experimental results are shown before and after applying GAN extensions to the traditional supervised FDD framework to illustrate the importance of GAN.
Abstract Supervised learning techniques have witnessed significant successes in fault detection and diagnosis (FDD) for heating ventilation and air-conditioning (HVAC) systems. Despite the good performance, these techniques heavily rely on balanced datasets that contain a large amount of both faulty and normal data points. In real-world scenarios, however, it is often very challenging to collect a sufficient amount of faulty training samples that are necessary for building a balanced training dataset. In this paper, we introduce a framework that utilizes the generative adversarial network (GAN) to address the imbalanced data problem in FDD for air handling units (AHUs). To this end, we first show the necessary procedures of applying GAN to increase the number of faulty training samples in the training pool and re-balance the training dataset. The proposed framework then uses supervised classifiers to train the re-balanced datasets. Finally, we present a comparative study that illustrates the advantages of the proposed method for FDD of AHU with various evaluation metrics. Our work demonstrates the promising prospects of performing robust FDD of AHU with a limited number of faulty training samples.
Unsupervised learning for fault detection and diagnosis of air handling units
Yan, Ke (author) / Huang, Jing (author) / Shen, Wen (author) / Ji, Zhiwei (author)
Energy and Buildings ; 210
2019-12-08
Article (Journal)
Electronic Resource
English
Air handling unit , Fault detection and diagnosis , Unsupervised learning , Generative adversarial network , AFDD , Automated Fault Detection and Diagnosis , AHU , Air Handling Unit , ARX , Auto-Regressive Model with Exogenous Variables , BBN , Bayesian Belief Network , CGAN , Conditional Generative Adversarial Network , CWGAN , Conditional Wasserstein Generative Adversarial Network , CWGAN-ELQCP , Conditional Wasserstein Generative Adversarial Network with Ensemble Learning Quality Control Protocol , DBN , Dynamic Bayesian Network , DR , Distance Rejection , DT , Decision Tree , ELQCP , Ensemble Learning Quality Control Protocol , EKF , Extended Kalman Filter , FDD , Fault Detection and Diagnosis , FP , False Positive , FN , False Negative , GAN , Generative Adversarial Network , HVAC , Heating Ventilation and Air-Conditioning , KF , Kalman Filter , KNN , K-Nearest-Neighbor , LSSVM , Least Square Support Vector Machine , MLP , Multi-Layer Perceptron , PCA , principal component analysis , ORL , Online Reinforcement Learning , QCP , Quality Control Protocol , RF , Random Forest , SVM , Support Vector Machine , TP , True Positive , TN , True Negative , WGAN , Wasserstein Generative Adversarial Network , XGBoost , Extreme Gradient Boosting
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