Development of a Neural Network-Based Fault Diagnostic System
Date Issued
August 2006
Abstract
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. In the prediction module an artificial neural network (ANN) is used, trained with
values obtained from a TRNSYS model of a fault-free system operated with the typical
meteorological year (TMY) files of Nicosia, Cyprus and Paris, France. Thus, the neural network is
able to predict the fault-free temperatures under different environmental conditions. The input data
to the ANN are the time of the year, various weather parameters 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.
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. In the prediction module an artificial neural network (ANN) is used, trained with
values obtained from a TRNSYS model of a fault-free system operated with the typical
meteorological year (TMY) files of Nicosia, Cyprus and Paris, France. Thus, the neural network is
able to predict the fault-free temperatures under different environmental conditions. The input data
to the ANN are the time of the year, various weather parameters 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.
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