GOLDGuaranteed Operation and Low
DMC
SEAMLESS AIRCRAFT HEALTH MANAGEMENT FOR A PERMANENT SERVICEABLE FLEET
Birmingham (UK) December 05, 2007
INASCOINASCO
A high-technology privately held industrial SME founded in 1989
Areas of expertise:
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)
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)
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.
Virtual Structural Health Management (VSHM™) platformVirtual Structural Health Management (VSHM™) platform
INASCO expertise related to GOLDINASCO expertise related to GOLD
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.
HM Model
Diagnostics
Uncertainties Load cases
Damage cases
Pseudo random signals
POD’s Damage distributions
Prognostics
Optimal Sensors topology
Probability of Failure.
Maintenance POF critical
Optimal Maintenance Schedule
Maintenenance Management scheme
This function is used for creating optimal maintenance strategy by elongating time intervals between inspections. The enlogation is performed with respect to certain POF
thresholds. Apart from design purposes, the certain function may also have as input real signal from sensors in order to compute the mainetenance schedule of an aircraft.
HM Model
Diagnostics
Uncertainties Load cases
Damage cases
Pseudo random signals
POD’s Damage distributions
Optimization
Sensors topology
Optimal sensors topology
Constraints
HM Optimization scheme.
Certain function is applied to maximize the sensitivity of the sensorized system to capture various anomalies (impact, cracks, etc.). Probability of Detection POD is
maximized with respect to various design constraints.
1st function
HM system topology optimisation for maximum defect detection capability
1st function
HM system topology optimisation for maximum defect detection capability
2nd function
Optimized Maintenance for low DMC and Structural Reliability.
2nd function
Optimized Maintenance for low DMC and Structural Reliability.
VSHM™ - Health Monitoring model: a precursor for Health ManagementVSHM™ - 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.
FEM analysis of a damaged composite fuselage part (model provided by Alenia, Ref: TANGO
project)
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)
Optimal Fibre Bragg Grating (FBG) placement into a composite part (Ref: SMIST project)
INASCO expertise related to GOLDINASCO expertise related to GOLD
Component levelStructure
&Sensing System
Deterministic modelsModeling of Structure and Sensing system
Modeling level
Stochastic model of sensorised
structure
Uncertaintiesgeometry, material, sensors
placement, loads, noise
VSHM™ - DiagnosticsVSHM™ - Diagnostics
INASCO expertise related to GOLDINASCO expertise related to GOLD
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.
PM or NDI s ignalwith anomaly
present
DataAcquisition
Correlates ignal to
anom aly s izeand type
PO SA
Constructinferences onquantiles and
dis tribution values
Set PO SA
Yes
OK
No
PM or ND I s ignalwith NO anomaly
present
D ataAcquis ition
Analyze s ignal
NO ISEDistribution
Constructinferences onquantiles and
dis tribution values
Set NO ISE
YesOK
No
POD and Anom alyDistributions
Set PO FA
Set PO D
Distribution ofDetected
Anom aly S izes
Distribution ofAnom aly Sizes
Store PO SA,NO ISE, PO FA,
PO D
Store
)(),( apapo
)(apo
)(ap
NewAnomalySize D ata
),;( apo
),...,,,,|( 21
^^
no aaaap
Updatedis tribution
U
Updating ofAnom aly
Distributions
Operation: PM and ND I Data Analys is
Description: Inferences
Reads: ND I and PM Data
Changes: PO D and D is tributions
Sends: Data to O ther Modules
Assum es:
Result: PO D and Anomaly D is tribution
PM or NDI s ignalwith anomaly
present
DataAcquisition
Correlates ignal to
anom aly s izeand type
PO SA
Constructinferences onquantiles and
dis tribution values
Set PO SA
Yes
OK
No
PM or ND I s ignalwith NO anomaly
present
D ataAcquis ition
Analyze s ignal
NO ISEDistribution
Constructinferences onquantiles and
dis tribution values
Set NO ISE
YesOK
No
POD and Anom alyDistributions
Set PO FA
Set PO D
Distribution ofDetected
Anom aly S izes
Distribution ofAnom aly Sizes
Store PO SA,NO ISE, PO FA,
PO D
Store
)(),( apapo
)(apo
)(ap
NewAnomalySize D ata
),;( apo
),...,,,,|( 21
^^
no aaaap
Updatedis tribution
U
Updating ofAnom aly
Distributions
Operation: PM and ND I Data Analys is
Description: Inferences
Reads: ND I and PM Data
Changes: PO D and D is tributions
Sends: Data to O ther Modules
Assum es:
Result: PO D and Anomaly D is tribution
“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)
“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)
Structural model
for part and embedded
sensors
Diagnostic toolexpert system,
use of virtual/real data
Prognostics tool
HEALTH MANAGEMEN
T
VSHM™ - OptimisationVSHM™ - Optimisation
INASCO expertise related to GOLDINASCO expertise related to GOLD
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 ManagementIn-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
JPDM : A “state of the art” probabilistic decision making tool applied on several NACRE studies
.
.
Optimal Latin Hypercube and Kriging Surrogate Model are the most efficient tools for MDO (HISAC project)
Optimal Latin Hypercube and Kriging Surrogate Model are the most efficient tools for MDO (HISAC project)
VSHM™ - PrognosticsVSHM™ - Prognostics
INASCO expertise related to GOLDINASCO expertise related to GOLD
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).
ADMIRE
VSHM™ - MaintenanceVSHM™ - Maintenance
INASCO expertise related to GOLDINASCO expertise related to GOLD
Sensor – advised Inspection Interval Assessment
Sensor – advised Inspection Interval Assessment
Interval assessment is elongated with the use of
probabilistic methods (Ref. IARCAS)
Interval assessment is elongated with the use of
probabilistic methods (Ref. IARCAS)
IARCAS
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.
Embedded sensors for structural health monitoringEmbedded sensors for structural health monitoring
INASCO expertise related to GOLDINASCO expertise related to GOLD
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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