Fault diagnostic method for a water heating system based on continuous model assessment and adaptation
Date Issued
October 2008
Abstract
The objective of this work is the development of an automatic solar water heater (SWH)
fault diagnostic system (FDS). The latter consists of a modelling module and a diagnosis
module. A data acquisition system measures the temperatures at four locations of the SWH
system (outlet of the water tank; inlet of the collector array; outlet of the collector array;
inlet of the water tank). In the modelling module a number of artificial neural networks
(ANN) are used, trained with the very first values when the system is fault free. Then, the
neural networks are able to predict the fault-free temperatures and compare them to actual
values. When the differences are low, the corresponding networks are unchanged. On the
contrary the networks are retrained. Then the diagnosis module analyses the difference
between the current connection weights and the initial weights. When a persistent significant
modification occurs, a flag is set to signify that a default is present in the SWH.
The system can predict three types of faults: collector faults and faults in insulation of the
pipes connecting the collector with the storage tank (to and from the tank) and these are
indicated with suitable labels. It is shown that all faults can be detected well before the end
of the drifts, without any false alarm, when the networks and thresholds are well tuned and
that the observation window has the right size. It is shown that this does not depend on the draw off profile.
fault diagnostic system (FDS). The latter consists of a modelling module and a diagnosis
module. A data acquisition system measures the temperatures at four locations of the SWH
system (outlet of the water tank; inlet of the collector array; outlet of the collector array;
inlet of the water tank). In the modelling module a number of artificial neural networks
(ANN) are used, trained with the very first values when the system is fault free. Then, the
neural networks are able to predict the fault-free temperatures and compare them to actual
values. When the differences are low, the corresponding networks are unchanged. On the
contrary the networks are retrained. Then the diagnosis module analyses the difference
between the current connection weights and the initial weights. When a persistent significant
modification occurs, a flag is set to signify that a default is present in the SWH.
The system can predict three types of faults: collector faults and faults in insulation of the
pipes connecting the collector with the storage tank (to and from the tank) and these are
indicated with suitable labels. It is shown that all faults can be detected well before the end
of the drifts, without any false alarm, when the networks and thresholds are well tuned and
that the observation window has the right size. It is shown that this does not depend on the draw off profile.
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