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- W755145179 abstract "Fault diagnostic methods aim to recognize when a fault exists on a system and to identifythe failures which have caused it. The fault symptoms are obtained from readings of sensorslocated on the system. When the observed readings do not match those expected then a faultcan exist. Using the detailed information provided by the sensors a list of the failures that are potential causes of the symptoms can be deduced. In the last decades, fault diagnosticshas received growing attention due to the complexity of modern systems and the consequentneed of more sophisticated techniques to identify failures when they occur. Detecting thecauses of a fault quickly and efficiently means reducing the costs associated with the systemunavailability and, in certain cases, avoiding the risks of unsafe operating conditions.Bayesian Belief Networks (BBNs) are probabilistic graphical models that were developed forartificial intelligence applications but are now applied in many fields. They are ideal formodelling the causal relations between faults and symptoms used in fault diagnostic processes.The probabilities of events within the BBN can be updated following observations(evidence) about the system state.In this thesis it is investigated how BBNs can be applied to the diagnosis of faults on asystem with a model-based approach. Initially Fault Trees (FTs) are constructed to indicatehow the component failures can combine to cause unexpected deviations in the variablesmonitored by the sensors. The FTs are then converted into BBNs and these are combinedin one network that represents the system. The posterior probabilities of the componentfailures give a measure of which components have caused the symptoms observed. The techniqueis able to handle dynamics in the system introducing dynamic patterns for the sensorreadings in the logic structure of the BBNs.The method is applied to two systems: a simple water tank system and a more complex fuelrig system. The results from the two applications are validated using two simulation codesin C++ by which the system faulty states are obtained together with the failures that causethem. The accuracy of the BBN results is evaluated by comparing the actual causes foundwith the simulation with the potential causes obtained with the diagnostic method." @default.
- W755145179 created "2016-06-24" @default.
- W755145179 creator A5033993153 @default.
- W755145179 date "2010-01-01" @default.
- W755145179 modified "2023-09-23" @default.
- W755145179 title "Application of Bayesian Belief Networks to system fault diagnostics" @default.
- W755145179 hasPublicationYear "2010" @default.
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