Fault diagnosis of an air-handling unit system using a dynamic fuzzy-neural approach

Authors:
Juan Du;Meng Joo Er;Leszek Rutkowski
Affiliations:
School of EEE, Nanyang Technological University, Singapore;School of EEE, Nanyang Technological University, Singapore;Department of Computer Engineering, Czestochowa University of Technology, Czestochowa, Poland and Academy of Management, SWSPiZ, Institute of Information Technology, Lodz, Poland
Venue:
ICAISC'10 Proceedings of the 10th international conference on Artificial intelligence and soft computing: Part I
Year:
2010

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Abstract

This paper presents a diagnostic tool to be used to assist building automation systems for sensor heath monitoring and fault diagnosis of an Air-Handling Unit (AHU). The tool employs fault detection and diagnosis (FDD) strategy based on an Efficient Adaptive Fuzzy Neural Network (EAFNN) method. EAFNN is a Takagi-Sugeno-Kang (TSK) type fuzzy model which is functionally equivalent to the Ellipsoidal Basis Function (EBF) neural network neurons. An EAFNN uses the combined pruning algorithm where both Error Reduction Ratio (ERR) method and a modified Optimal Brain Surgeon (OBS) technology are used to remove the unneeded hidden units. Simulation works show the proposed diagnosis algorithm is very efficient which can not only reduce the complexity of the network but also accelerate the learning speed.