Jenions

5/12/2018 Jenions - slidepdf.com

http://slidepdf.com/reader/full/jenions 1/31

Rolls-Royce data ± strictly private

5 Steps to Heaven ±A Rolls-Royce Perspective

IBM Almaden Institute, San Jose7th May 2008

Dr Ian K Jennions

EHM Global Capability Owner

©2008 Rolls-Royce plcThe information in this document is the property of Rolls-Royce plc and may not be copied or communicated to a third party, or used for any purpose other than that for

which it is supplied without the express written consent of Rolls-Royce plc.

This information is given in good faith based upon the latest information available to Rolls-Royce plc, no warranty or representation is given concerning suchinformation, which must not be taken as establishing any contractual or other commitment binding upon Rolls-Royce plc or any of its subsidiary or associated

companies.



2

© 2008 Rolls-Royce plc

5 Steps to Heaven ± A Rolls-Royce

Perspective

Introduction

Our Service Business

Asset Management

Business Landscape

5 Steps to Heaven

Simulation of Total Care

Value in Data

Technology Successes

Marine EHM (Equipment

Health Management)

AE3007 HP Vane

QUICK

Future

Current Issues

Summary



3


Rolls-Royce¶s four markets



4


Rolls-Royce Group plc - 2007



5


Video: µNew Thinking, Joined Up Thinking'



6



Perspective

Introduction


Asset Management

Business Landscape

5 Steps to Heaven


Value in Data



Health Management)

AE3007 HP Vane

QUICK

Future

Current Issues

Summary



7


Changing Customer Requirements

Traditional Support

Spare parts

Repair & overhaul

Enhanced Support

Data & forecastingservices

Technical & logisticssupport

Customer training

Advanced Support

Comprehensivepackage integratingelements of basic andenhanced support

Spares inclusive R&Ocontracts

Total Support

Complete,availability- basedservice. Can cover all off-aircraft, andsome on-aircraftactivity

Configurationmanagement &reliabilityenhancementscovered

Extended Support

Partnered capability tocover complete weaponsystems

Turn-key services

Non-propulsion relatedsupport solutions

Increased Scope

C u s t o m e

r V a l u e

R e

a c t i v e

A l i g n

e d

I n t e g r a t e d



8


The EHM Transformation Staircase - Defence

Basic EHM

- Lifing ± cyclic counts- Digital borescope

Advanced EHM- Lifing- Performance trending- Oil trending- Vibration trending- Improved life counting

- Basic diagnostics

Total Care EHM- Data fusion ±

oil/performance/vibration- Integration with LCC &

WSG tools- Advanced diagnostic- Dynamic performance

model (COMPASS)

Customer ExploitsCustomer ExploitsInfo. to Reduce CostInfo. to Reduce Cost

Extended EHM- Piece part diagnostics- Prognostic capability- Networking of systems

(BROADEN)

Future EHM

- Unknown at this time,but could includePowerplant and/or platform healthmonitoring

CommodityCommodity ServiceService

RR Exploits Info. to IncreaseRR Exploits Info. to IncreaseCost Effectiveness of ProductCost Effectiveness of Product

Stage 1

Stage 2

Stage 3

Stage 4

Stage 5

BasicSupport

Traditional

EnhancedSupport

Forecasting

TotalSupport

Engine Availability

AdvancedSupport

AssetManagement

Extended Support

Total Management

Customer RiskCustomer Risk Service Provider RiskService Provider Risk



9


Total Care Simulation - Example

Product Operation

aircraft, engine, airport,operations control

Product Behaviour

engine, module, part, virtualcharacteristic

Asset Management parts, modules, dedicated &

lease pool spare engines,operator interaction

Manufacturing & Supply aircraft, engines, modules,

parts

Transportation & Logistics engines, modules, parts

Engine Repair, Overhaul & Test capacity, capability, resourcing,

availability

Component & Module Repair



0


Total Care Simulation- video



1


± ± ±

Integrated Products and Services (IPAS)

± ± ±

Service Engineers/Overhaul trained andmotivated to share knowledge with design.Tools, methods and processes available tosupport data and knowledge capture

Product designed to support the service offering ±existing service knowledge base fully used,continuous improvement via optimized IPASProcesses

Designer able to search easily for knowledge via intuitive interface,ontology models and socialsolutions. Able to do LCC trade-offs, understands service needs

Designer

Knowledge

Desktop (WP1)

LCC Modelling Toolkit

(WP3)

Service Data

Product Designer

Service Engineer/

Overhaul Shop

Social Issues and Solutions

(WP1)

±

Customer Customer

Customer

Optimised IPAS Processes

Key:

Service Engineers/Overhaul trained andmotivated to share knowledge with design.Tools, methods and processes available tosupport data and knowledge capture

Product designed to support the service offering ±existing service knowledge base fully used,continuous improvement via optimized IPASProcesses

Designer able to search easily for knowledge via intuitive interface,ontology models and socialsolutions. Able to do LCC trade-offs, understands service needs

Designer

Knowledge

Desktop (WP1)


(WP3)

Service Data

Designer

Knowledge

Desktop


Service Data

Product Designer Product Designer

Service Engineer/

Overhaul Shop

Service Provider Service Provider

Social Issues and Solutions

New (IPAS)

Existing

New (IPAS)

Existing

±

Customer Customer Customer

New (IPAS)New (IPAS)

Knowledge flow



12


The IPAS System Architecture

Knowledge

Desktop

SDMSAP RCP Reports Other

LegacyDoc

LegacyData

Source2

NewDoc

NewData

Source4

ETC Data

Information

Knowledge

K-Search

K-Forms

Mechanisms

Features

Parts

Materials

Data Types

Ontologies

K-Forms

WebServices



13


Legacy data is largely unstructured and is captured and stored in a variety of

ways.

Development of ontologies for hardware description, failure mechanism etc.

allow us to make sense of unstructured legacy data.

K-Search provides a means of interrogating legacy data and presenting

structured search results back to the user

K-Search is a learning system that builds an understanding of document

content such that after the µlearning process¶ >95% rate of correct automateddocument content classification can be achieved.

IPAS Results ± the ability to search legacy data

Automatically

extracting

knowledge from

legacy unstructured

data



14



Perspective

Introduction


Asset Management

Business Landscape

5 Steps to Heaven


Value in Data



Health Management)

AE3007 HP Vane

QUICK

Future

Current Issues

Summary



15


Marine Off-shore Supply and Service Vessel

Propulsion units monitored:

4 Bergen 6 cylinder Cengines.

2 Azipull thrusters.

2 Tunnel thrusters. 1 swing up thruster.



16


Ethernet

Propulsion

EHM unit

Engines

EHM unit

Thrusters

EHM unit

Engines

Data Coll. unit

RRM

Ship network

EHMCentral unit

RRM

Service Centre

Vessel 49 EHM

Network Installation.



17


AE3007 HPT Vane Distress

Leading edge burns away over time

Causes: cooling blockage, oxidation

Condition that typically occurs on high time engines(primarily on-condition engines)

Causes increased stress on the 1st stage blade leading tohigh cycle fatigue (secondary damage)

Turbine blade fracture leads to in flight shut down and a 2xshop visit cost



18


AE3007 HPT Vane Distress

Vane distress showed up as performance changes. Data fusion allows clear signature to be set

Fuel flow, turbine temp, pressure & speed 50 hour window after detection with 96% confidence of

preventing HPT blade failures Allows time to schedule removal Thresholds set to balance false positives and false negatives

Relatively small number of false alerts and false negatives



19


AE3007 HPT Vane Distress - Savings

The alternative of not having EHM for this issue

Allow HPT-1 blade failures

Pull engines in mod campaign

Implement a 50 hr boroscope inspection interval

8 yr EHM Savings estimated at over $60M Based on mod campaign or inspections both ($$$$)

Savings continue as engines age and experience vanedistress



20


So What¶s the Problem ?

With high-integrity systems such as jet engines, conventional faultdetection methods have limited capability:

The most important examples (i.e. the abnormalities) are very rare Some of the fault conditions may not have been seen before Subtle pre-cursor events often exist prior to failure but these arerarely detected by employing conventional techniques.

Advances being made at RR via advanced computational and signalprocessing methods, specifically:

QUICK, learn a description of normality and test against it (OBS) Crack detection and propagation (Cranfield University) Knowledge Based Diagnostics (Sheffield University)

DAME (Universities of York, Leeds, Sheffield and Oxford)



21


Quantitative User friendly IntelligentCollection of Knowledge (QUICK)

QUICK is a system used for real time acquisition and intelligent analysisof vibration and performance data from gas turbine engines.

QUICK has been developed over a number of years between OxfordUniversity, Oxford BioSignals and Rolls-Royce

The innovation in the product lies in:

The use of Computational Intelligence to quantify features

Building models of normality

Fusion of data from disparate sources for engine diagnostics.

The system is exploitable in other fields (e.g. medical)



22


QUICK video: Innovation Award



23


Novelty ThresholdsNovelty Thresholds ± ± Bayesian EVTBayesian EVT

2323



24

© 2008 Rolls-Royce plc 2424

Example resultsExample results ± ± 826 tests826 tests

Method 1: Bayesian EVT Method 2: Classical EVT Method 3: Expert Heuristic

Data Set Sensitivity Specificity Sensitivity Specificity Sensitivity Specificity

A1 0.75 1.00 0.75 0.97 0.75 0.93

A2 1.00 1.00 1.00 0.90 1.00 1.00

B1 - 1.00 - 0.99 - 0.99

B2 - 1.00 - 0.85 - 0.45

B3 - 0.99 - 0.70 - 0.68

B4 - 1.00 - 0.70 - 0.65

Overall 0.78 0.99 0.78 0.83 0.78 0.73



25



Perspective

Introduction


Asset Management

Business Landscape

5 Steps to Heaven


Value in Data



Health Management)

AE3007 HP Vane

QUICK

Future

Current Issues

Summary



26


Different perspectives ± system boundaries



27


EHM Intelligence Dichotomy

Fleet centred view - the realisation of full value by having an accurate, real time fleet view of the

state of all assets

Platform centric view - Devolving EHM intelligence to the local platform to compensate for:

± limitations in communicating with a central information hub (a lack of infrastructure andcapacity)

± Requirements for assets to operate and be maintained autonomously

± Concerns for data privacy and IPR

LOCAL PLATFORM

Often has to operatedeployed and autonomously,

and often has an ability toself maintain. If the platform

is mobile, datacommunications may be

limited

Data Communications infrastructure

CENTRAL HUB

Processes EHM datacentrally and issuesalerts. Maintains an

overview of the conditionof the full fleet. Is able tomitigate the operational

consequences of failure

LOCAL PLATFORM


and often has an ability toself maintain. If the platform

is mobile, datacommunications may be

limited

LOCAL PLATFORM


and often has an ability toself maintain µon mission¶. If the platform is mobile, datacommunications channels

may change and be limited

Communicationsbandwidth may belimited and may be

expensive

Real time monitoring offers advantages of better resolution, earlier detection and a

wider scope of failure mode detection - at

the price of having to process muchhigher data volumes

Some organizationsdo not want to export

their data beyondtheir firewalls

THE RESULT IS PRESSURE TO PUSH EHM INTELLIGENCE TOWARD THE PLATFORM



28


Vision: HUMS Information Fused with Operations for Optimised Fleet Management

SAP

CoreControlCoreAlert

Service Data Manager Aero Exec Integrator

Gold (T45 Programme)



29


Vision: The Operations Room of the Future?

Oil uplift and utilizationdata transferred from

Squadron and pilot reports

EHUMS data download from A/Cusing data retrieval unit (DRU)

routed via Ground Support System(GSS) to Operations Centre

Customers alerted by pager, e-mailor cell phone

Customers can view statuson DefenceManager

Operations Room - processesinformation

Service team monitors output andliases with customer

Repair andOverhaul

Agent-based aftermarketmodelling and decision

support

All Operations linked throughglobal GRID system

AI fault recognitionand diagnosis



30


5 Steps to Heaven - Summary

In the OEM to Service business transformation data is key

Operating on the data to create information and knowledgehas been demonstrated, including value, in a number of areas.

With risk shift from Customer to OEM in a Service modelEHM is risk mitigation, informing the Aftermarket business andhelping to minimise unplanned events

Basic EHM has been yielding good results for a number of years, the next steps are much more complicated



31

© 2008 Rolls-Royce plc http://www.cranfield.ac.uk/cua

Vision A world-class research and demonstration Centre of Excellence,seeking to establish industry standards and yielding competitive

advantages in Integrated Vehicle Health Management (IVHM) for i tsindustry members.

Strategic Aims Attract Investment ± supplement launch funding by attracting

additional investment from new core partners/members and researchfunding bodies

World Recognition ± working with industry and academic leaders

Technology Transfer ± implementation of concepts and technologieswithin the industry and develop the associated skills base Self Sustaining ± after 5 years

Delivery of Competitive Advantage ± to the key stakeholders

Cranfield Integrated Vehicle HealthManagement Centre of Excellence

Generic Architecture

CoE Concept Funding/Governance

Jenions

Documents

Transcript of Jenions