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ARX model based fault detection and diagnosis for chillers using support vector machines
Highlights Proposed chiller FDD method combines ARX and SVM. Comparison with pure data-driven, regression+SVM, and ANN. Our method outperforms alternatives (higher accuracy and lower false alarm). We use only six variables (three independent and three dependent), far fewer than others. After training for generic chiller types, algorithms can be integrated in BMS.
Abstract Efficient and robust fault detection and diagnosis (FDD) can potentially play an important role in developing building management systems (BMS) for high performance buildings. Our research indicates that, in comparison to traditional model-based or data-driven methods, the combination of time series modeling and machine learning techniques produces higher accuracy and lower false alarm rates in FDD for chillers. In this paper, we study a hybrid method incorporating auto-regressive model with exogenous variables (ARX) and support vector machines (SVM). A high dimensional parameter space is constructed by the ARX model and SVM sub-divides the parameter space with hyper-planes, enabling fault classification. Experimental results demonstrate the superiority of our method over conventional approaches with higher prediction accuracy and lower false alarm rates.
ARX model based fault detection and diagnosis for chillers using support vector machines
Highlights Proposed chiller FDD method combines ARX and SVM. Comparison with pure data-driven, regression+SVM, and ANN. Our method outperforms alternatives (higher accuracy and lower false alarm). We use only six variables (three independent and three dependent), far fewer than others. After training for generic chiller types, algorithms can be integrated in BMS.
Abstract Efficient and robust fault detection and diagnosis (FDD) can potentially play an important role in developing building management systems (BMS) for high performance buildings. Our research indicates that, in comparison to traditional model-based or data-driven methods, the combination of time series modeling and machine learning techniques produces higher accuracy and lower false alarm rates in FDD for chillers. In this paper, we study a hybrid method incorporating auto-regressive model with exogenous variables (ARX) and support vector machines (SVM). A high dimensional parameter space is constructed by the ARX model and SVM sub-divides the parameter space with hyper-planes, enabling fault classification. Experimental results demonstrate the superiority of our method over conventional approaches with higher prediction accuracy and lower false alarm rates.
ARX model based fault detection and diagnosis for chillers using support vector machines
Yan, Ke (author) / Shen, Wen (author) / Mulumba, Timothy (author) / Afshari, Afshin (author)
Energy and Buildings ; 81 ; 287-295
2014-05-21
9 pages
Article (Journal)
Electronic Resource
English
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