The application of artificial neural networks (ANNs) for developing a fault diagnosis (FD) method in complex heating systems is presented. The six operating modes with faults used to develop this FD method came from the results of a detailed investigation in cooperation with heating system maintenance experts and are among the most important operating faults for this type of system. Because a daily diagnosis is generally sufficient, the ANNs have been developed using the daily values obtained by a preprocessing of the numerical simulation data. Presents the first step of the method development. It demonstrates the feasibility of using ANNs for fault diagnosis of a specific heating, ventilating, and air-conditioning (HVAC) system provided training data representative of the behaviour of the system with and without faults are available. The next step will consist of developing a generic method that requires less training data.

KEYWORDS: year 1996, failure, detectors, heating, ventilation, air conditioning, expert systems, calculating, networks