Development of a neural network-based fault diagnostic system for solar thermal applications

The objective of this work is to present the development of an automatic solar water heater (SWH) fault diagnosis system (FDS). The FDS system consists of a prediction module, a residual calculator and the diagnosis module. A data acquisition system measures the temperatures at four locations of the SWH system and the mean storage tank temperature. In the prediction module a number of artificial neural networks (ANN) are used, trained with values obtained from a TRNSYS model of a fault-free system operated with the typical meteorological year (TMY) for Nicosia, Cyprus and Paris, France. Thus, the neural networks are able to predict the fault-free temperatures under different environmental conditions. The input data to the ANNs are various weather parameters, the incidence angle, flow condition and one input temperature. The residual calculator receives both the current measurement data from the data acquisition system and the fault-free predictions from the prediction module. The system can predict three types of faults; collector faults and faults in insulation of the pipes connecting the collector with the storage tank and these are indicated with suitable labels. The system was validated by using input values representing various faults of the system.