01897oam 2200517I 450 991070618580332120170821114001.0(CKB)5470000002454566(OCoLC)703661048(EXLCZ)99547000000245456620110224j199911 ua 0engurbn|||||||||txtrdacontentcrdamediacrrdacarrierPropellant feed system leak detection lessons learned from the linear aerospike SR-71 experiment (LASRE) /Neal Hass [and five others]Edwards, California :National Aeronautics and Space Administration, Dryden Flight Research Center,November 1999.1 online resource (32 pages) illustrationsNASA/TM ;1999-206590"November 1999.""Presented at the AIAA 9th International Space Planes and Hypersonic Systems Conference, Norfolk, Virginia, November 1-5, 1999"--Report documentation page."Performing organization: NASA Dryden Flight Research Center"--Report documentation page.Includes bibliographical references (page 14).Propellant feed system leak detection LeakagenasatAerospike enginesnasatGas detectorsnasatPressure sensorsnasatFeed systemsnasatFlight testsnasatLeakage.Aerospike engines.Gas detectors.Pressure sensors.Feed systems.Flight tests.Hass Neal1413657Dryden Flight Research Facility,SSNSSNGPOBOOK9910706185803321Propellant feed system leak detection3510558UNINA03579nam 2200805z- 450 991055763040332120210501(CKB)5400000000045110(oapen)https://directory.doabooks.org/handle/20.500.12854/68494(oapen)doab68494(EXLCZ)99540000000004511020202105d2021 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierAlgorithms for Fault Detection and DiagnosisBasel, SwitzerlandMDPI - Multidisciplinary Digital Publishing Institute20211 online resource (130 p.)3-0365-0462-1 3-0365-0463-X Due to the increasing demand for security and reliability in manufacturing and mechatronic systems, early detection and diagnosis of faults are key points to reduce economic losses caused by unscheduled maintenance and downtimes, to increase safety, to prevent the endangerment of human beings involved in the process operations and to improve reliability and availability of autonomous systems. The development of algorithms for health monitoring and fault and anomaly detection, capable of the early detection, isolation, or even prediction of technical component malfunctioning, is becoming more and more crucial in this context. This Special Issue is devoted to new research efforts and results concerning recent advances and challenges in the application of "Algorithms for Fault Detection and Diagnosis", articulated over a wide range of sectors. The aim is to provide a collection of some of the current state-of-the-art algorithms within this context, together with new advanced theoretical solutions.History of engineering and technologybicsscadaptive template matchingamplitude-aware permutation entropybattery faultsbattery management systembattery safetycombined predictiondamagedigital image processingedge detectionfault diagnosisfault diagnostic algorithmsfault predictiongenetic algorithmgray level co-occurrence matrixinstantaneous angular speedKalman filterleast squares support vector machinelithium-ion batterymachine diagnosticsmachine visionmisalignmentmotion trackingmultiscale entropymultivariate grey modelparametric template modelingquantum genetic algorithmrandom forestreusable launch vehiclerolling bearingsself-organization mapstructural health monitoringSURVISHNO 2019 challengethruster fault detectionthruster valve failurevideo tachometerHistory of engineering and technologyFerracuti Francescoedt1280545Freddi AlessandroedtMonteriù AndreaedtFerracuti FrancescoothFreddi AlessandroothMonteriù AndreaothBOOK9910557630403321Algorithms for Fault Detection and Diagnosis3017193UNINA