| People | Locations | Statistics |
|---|---|---|
| Ziakopoulos, Apostolos | Athens |
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| Vigliani, Alessandro | Turin |
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| Catani, Jacopo | Rome |
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| Statheros, Thomas | Stevenage |
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| Utriainen, Roni | Tampere |
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| Guglieri, Giorgio | Turin |
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| Martínez Sánchez, Joaquín |
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| Tobolar, Jakub |
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| Volodarets, M. |
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| Piwowar, Piotr |
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| Tennoy, Aud | Oslo |
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| Matos, Ana Rita |
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| Cicevic, Svetlana |
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| Sommer, Carsten | Kassel |
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| Liu, Meiqi |
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| Pirdavani, Ali | Hasselt |
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| Niklaß, Malte |
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| Lima, Pedro | Braga |
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| Turunen, Anu W. |
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| Antunes, Carlos Henggeler |
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| Krasnov, Oleg A. |
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| Lopes, Joao P. |
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| Turan, Osman |
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| Lučanin, Vojkan | Belgrade |
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| Tanaskovic, Jovan |
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Sbarufatti, Claudio
in Cooperation with on an Cooperation-Score of 37%
Topics
- modeling
- helicopter
- qualification
- fatigue cracking
- structural health monitoring system
- assessment
- estimate
- landing
- chemical element
- behavior
- maintenance
- monitoring
- validation
- altitude
- synthetic
- system safety
- fuselage
- algorithm
- velocity
- neural network
- sensor
- face
- filtration
- strain gage
- bird
- bird strike
- honeycomb structure
- impact test
- aircraft
- health
- structural health monitoring
- cracking
- aluminum
- optimisation
- data
- estimating
- diagnosis
- alarm system
- strain measurement
- show 9 more
Publications
- 2017Analysis of a helicopter main gearbox by means of numerical modelling approach
- 2016Model-Assisted performance qualification of a distributed SHM system for fatigue crack detection on a helicopter tail boom
- 2016Experimental validation of a computational hybrid methodology to estimate fuselage damage due to harsh landingcitations
- 2015Model-based structural integrity assessment of helicopter fuselage during harsh landing
- 2015Low-velocity impact monitoring system for a helicopter frame by means of an artificial neural network
- 2013MEMS for structural health monitoring in aircraftcitations
- 2013Helicopter harsh landing events: A computational hybrid methodology to estimate fuselage damagecitations
- 2013Artificial neural networks for structural health monitoring of helicopter harsh landingscitations
- 2012Diagnostic system validation for damage monitoring of helicopter fuselage
- 2012ANN based Bayesian hierarchical model for crack detection and localization over helicopter fuselage panels
- 2011Sensor network optimization for damage detection on aluminium stiffened helicopter panels
- 2010Probability of detection and false alarms for metallic aerospace panel health monitoring
Places of action
conferencepaper
Probability of detection and false alarms for metallic aerospace panel health monitoring
Abstract
The continuing shift toward the damage tolerant design approach is powering the research on Structural Health Monitoring Systems (SHMS). A key aspect is the research in the field of sensor equipment configurations in order to produce the most reliable and easily affordable system to realize the on-board structural health diagnosis, thus setting the basis for the Condition Based Maintenance (CBM). The evaluation of the sensor network competence in detecting a crack growing over the monitored structure is one of the most challenging issues. The proposed document, developed contextually to a wider project (HECTOR) financed and promoted by EDA, is aimed to provide a methodology to evaluate the economical affordability of SHM on helicopter fuselage panels, through the combined use of the Probability of False Negatives (PFN) and False Alarms (PFA), thus estimating the Probability of Detection (PoD). A statistical FEM-based method is proposed to model the sensibility of one sensor to a growing crack, in function of the crack size and location, as well as depending on the sensor position over the panel. A comparison is also carried out with experimental data relative to strain measurements over different panel locations and in function of the growing semi-crack lengths. A good detection capability is found for some strain sensors, even when located far from the occurring damage. 1. Introduction
Topics
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