| People | Locations | Statistics |
|---|---|---|
| Naji, M. |
| |
| Motta, Antonella |
| |
| Aletan, Dirar |
| |
| Mohamed, Tarek |
| |
| Ertürk, Emre |
| |
| Taccardi, Nicola |
| |
| Kononenko, Denys |
| |
| Petrov, R. H. | Madrid |
|
| Alshaaer, Mazen | Brussels |
|
| Bih, L. |
| |
| Casati, R. |
| |
| Muller, Hermance |
| |
| Kočí, Jan | Prague |
|
| Šuljagić, Marija |
| |
| Kalteremidou, Kalliopi-Artemi | Brussels |
|
| Azam, Siraj |
| |
| Ospanova, Alyiya |
| |
| Blanpain, Bart |
| |
| Ali, M. A. |
| |
| Popa, V. |
| |
| Rančić, M. |
| |
| Ollier, Nadège |
| |
| Azevedo, Nuno Monteiro |
| |
| Landes, Michael |
| |
| Rignanese, Gian-Marco |
|
Osigwe, Emmanuel
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (1/1 displayed)
Places of action
| Organizations | Location | People |
|---|
document
Integrated Gas Turbine System Diagnostics: Components and Sensor Faults Quantification using Artificial Neural Network
Abstract
The role of diagnostic systems in gas turbine operations has changed over the past years from asinglesupport troubleshootingmaintenancetoamoreproactiveintegrateddiagnosticsystem.Thishasbecomeso,because detectingandfixingfault(s)ononegasturbinesub-systemcantriggerfalsefault(s)indication,onother component(s) of thegas turbine system,due to interrelationships between data obtained to monitor not only the GTsinglecomponent,butalsotheintegratedcomponentsandsensors.Hence,thereisneedforintegrationof gas turbinesystemdiagnostics.Thepurposeofthispaperistopresentartificialneuralnetworkdiagnostic system(ANNDS)asanintegratedgasturbinesystemdiagnostictoolcapableofquantifyinggasturbinecomponentandsensorfault.Amodelbasedapproachwhichconsistsofanenginemodel,andanassociated parameterestimationalgorithmthatpredictsthedifferencebetweentherealenginedataandtheestimated outputdataisdescribedinthispaper. TheANNDSsystemwastrainedtodetect,isolateandassess component(s) and sensor fault(s) of a singlespool industrial gas turbine GT-PG9171ER. The ANN modelwas construedwith multi-layer feed-forward back propagation network for component fault(s) and auto associative networkforsensorfault(s).Thediagnosticmethodologyadoptedwasanestednetworkstructure,trainedto handlespecificobjectivefunctionofdetecting,isolatingorquantifyingfaults.Thedatausedfortraining,and testingpurposeswereobtainedfromanon-linear aero-thermodynamicmodelusingPYTHIA;aCranfieldUniversityin-housesoftware.Thedatasetanalyzedinthispaperrepresentsamplesofcleanandfaulty gas turbinecomponentscausedbyfouling(0.5% -6% degradation)andsensorfault(s)duetobias (±1% -±7%). The results show the capability of ANN to detect, isolate (classification) and quantify multiple faults if properly trained <br/><br/>Integrated Gas Turbine System Diagnostics: Components and Sensor Faults Quantification using Artificial Neural Network | Request PDF. Available from: https://www.researchgate.net/publication/319645027_Integrated_Gas_Turbine_System_Diagnostics_Components_and_Sensor_Faults_Quantification_using_Artificial_Neural_Network [accessed Oct 24 2018].