GOLD Guaranteed Operation and Low DMC SEAMLESS AIRCRAFT HEALTH MANAGEMENT FOR A PERMANENT...

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)


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


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



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



Set PO SA

Yes

OK

No

PM or ND I s ignalwith NO anomaly

present

D ataAcquis ition

Analyze s ignal

NO ISEDistribution



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


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


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


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


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

GOLD Guaranteed Operation and Low DMC SEAMLESS AIRCRAFT HEALTH MANAGEMENT FOR A PERMANENT...

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