Automatic feature selection using multiobjective cluster optimization for fault detection in a heating ventilation and air conditioning system

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Conference Proceeding
Proceedings - 1st International Conference on Artificial Intelligence, Modelling and Simulation, AIMS 2013, 2014, pp. 171 - 176
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© 2013 IEEE. The performance of Automatic Fault Detection and Diagnostics (AFDD) algorithms to identify faults in complex building Heating Ventilation and Air-Conditioning (HVAC) systems depend on the appropriateness of features. This paper proposes a knowledge-discovery approach for discovering characteristic features using Multi-Objective Clustering Rapid Centroid Estimation (MOC-RCE). The proposed method has been tested on experimental fault data from the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) research project 1312-RP Winter 2008 dataset. An experiment involving 100 clustering trials shows that using the proposed method, on average 15 characteristic features have been selected from the original 320 features. Sensitivity, specificity, accuracy, precision, and F-score values of greater than 95% are achieved with the provided features.
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