A connectionist approach to the development of a fault detection and diagnosis method for hydraulic heating systems

The application of artificial neural networks for developing a fault detection and diagnosis method in complex heating systems is presented in this paper. The 6 operating modes with faults used to develop this tool stemmed from the results of a detailed investigation in co-operation with heating systems maintenance experts, and are among the most important operating faults for this type of system. This paper presents the method development by simulation data. It demonstrates the feasibility of using neural networks for fault detection and diagnosis of a specific heating system provided training data representative of the behaviour of the system with and without faults are available.