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GOLD G uaranteed O peration and L ow D MC

GOLD G uaranteed O peration and L ow D MC. SEAMLESS AIRCRAFT HEALTH MANAGEMENT FOR A PERMANENT SERVICEABLE FLEET. Birmingham (UK) December 05, 2007. INASCO. A high-technology privately held industrial SME founded in 1989 Areas of expertise:. Company overview:

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GOLD G uaranteed O peration and L ow D MC

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  1. GOLD Guaranteed Operation and Low DMC SEAMLESS AIRCRAFT HEALTH MANAGEMENT FOR A PERMANENT SERVICEABLE FLEET Birmingham (UK) December 05, 2007

  2. INASCO A high-technology privately held industrial SME founded in 1989 Areas of expertise: • Company overview: • 20 Top rate researchers/developers • Multidisciplinary expertise: Process Monitoring Sensors, Composites Manufacturing, • Materials Science, CAD/CAM, Engine Noise Control • 1,5 m€ per annum for the last 2 years invested in New Research Studies and Technologies • development • 2 m€ investment on new manufacturing plant for high-end aerospace components • (commencing manufacturing activities in 3 quarter of 2009)

  3. Sensorised aero – structure design demands numerous multidisciplinary requirements. A Health Management Software that that will reduce DMC in different levels of operation (Component, Aircraft and Fleet) can be developed to treat this situation. A Health Management Platform can be realized by performing a series of steps which include Structural Analysis, Economic Modeling and Decision Making techniques. The HM Software will be able to provide guidelines for minimum DMC and increased Operational Safety, and useful Data for Operators.

  4. INASCO expertise related to GOLD Virtual Structural Health Management (VSHM™) platform VSHM comprises a “state of the art” tool with capability to design a robust, efficient and viable HM system by taking account uncertainty arising from the manufacturing and operational phase of the component and/or aircraft. VSHM will aid in the analysis, optimisation and evaluation of various structural Health Monitoring (HM) concepts and Maintenance strategies from early design phase. 1st function HM system topology optimisation for maximum defect detection capability 2ndfunction Optimized Maintenance for low DMC and Structural Reliability.

  5. INASCO expertise related to GOLD VSHM™ - Health Monitoring model: a precursor for Health Management Modeling and simulation of the operational behavior of various sensors for any damage – load case by quantifying environmental or structural uncertainty. Component level Structure & Sensing System Modeling level Deterministic models Modeling of Structure and Sensing system Stochastic model of sensorised structure Uncertainties geometry, material, sensors placement, loads, noise FEM analysis of a damaged composite fuselage part (model provided by Alenia, Ref: TANGO project) Optimal Fibre Bragg Grating (FBG) placement into a composite part (Ref: SMIST project)

  6. INASCO expertise related to GOLD VSHM™ - Diagnostics • Probability of Detection (POD) and damage characterisation will be quantified. • POD : the probability of the sensorised system to • capture various defects on a damaged structure. • Defects are characterised using statistical distributions • for damage type, size, location, impact energy, etc. • Optimisation, reverse engineering or “expert” methods will • be used to determine the correlations between sensors • signals and defect parameters. Structural model for part and embedded sensors Diagnostic tool expert system, use of virtual/real data Prognostics tool HEALTH MANAGEMENT “Expert” Module will be capable of calculating Damage distributions and Probability of Detection using signal from different types of sensors. Method is developed for Manufacturing Process Monitoring and NDI, and it will be extended for HM applications. (Ref. MANHIRP project)

  7. INASCO expertise related to GOLD Optimal Latin Hypercube and Kriging Surrogate Model are the most efficient tools for MDO (HISAC project) VSHM™ - Optimisation Maximize POD by performing sensors topology optimisation with respect to various constraints rising from sensor placement, operational cost, data acquisition and wiring. Improvement of Health Management In-house decision making (Joint Probabilistic Decision Making) and optimisation tools (Multidisciplinary Design Optimisation) will lead to optimisation of Health Management scenaria (sensors types, location, orientation). JPDM : A “state of the art” probabilistic decision making tool applied on several NACRE studies

  8. ADMIRE INASCO expertise related to GOLD VSHM™ - Prognostics Prometheus: Probabilistic Structural Analysis and Reliability tool - load cases of the operational phase of an A/C. Stochastic models will be used to predict the Probability of Structural Failure by means of the validated diagnostic tool (defects characterisation information). Prometheus Software is an in-house Probabilistic Design software tool. Its modules have been successfully applied on various probabilistic structural analysis problems such as: i) fatigue crack growth Reliability and Sensitivity Analysis and ii) ageing prediction of various aircraft components (Ref: ADMIRE, RAMGT, TATEM).

  9. IARCAS INASCO expertise related to GOLD VSHM™ - Maintenance Sensor – advised Inspection Interval Assessment The calculated probabilities will guide the decision upon the Maintenance Schedule. Novel sensor – advised maintenance methodology will be applied in order to elongate the interval between inspections. Minimisation of the maintenance costs without exceeding a critical value of Probability of Structural Failure. Interval assessment is elongated with the use of probabilistic methods (Ref. IARCAS)

  10. INASCO expertise related to GOLD Embedded sensors for structural health monitoring • Sensorised structures: • embedded sensing fabrics • embedded sensing skins (“smart skins”) • non embedded sensing skins (“smart skins” which could be applied externally on the part surface during inspection phase • Expected features: • multitude of information (different sensors co-existing in the same substrate) • embedded sensor part of the design (sensor placement and capabilities are design parameters) • customisable/flexible according to part geometry • Enabling technologies: • FBGs (low diameter) • micro – sensors • direct writing • Challenges: • sensors miniaturisation • signal processing • real time data acquisition Scale up methodology: sensor  sensor node  sensor array  smart fabric or skin

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