Predictive Maintenance & IoT a Capgemini POV

Predictive Maintenance & IoT

Capgemini 2017

2The information contained in this document is proprietary. Copyright © 2016 Capgemini. All rights reserved.

PegaWorld, Las Vegas | June 2016In collaboration with

Business lives in the - Everything sensored, connected, analyzed & mobile



Turning Data into The Internet of Connected Smart Things …



… is about achieving Smart Outcomes

Labor productivity

Material / inventory turns

Component reliability (NFF)

Increased asset availability

Lower risk for safety events

Improving first time fix rates

Accurate & Precise Knowledge

Actionable Decision Support

Faster Smart Profitable Service



Capgemini Approach to Predictive Maintenance

• Harness the power of engineering data for aftermark et operations

� Design reliability data

� Part drawing, assembly data

� Bills of Materials

� Supplier Information

• Build an infrastructure handle large data and for connected product data in real-time

• Complete knowledge of configuration of parts includingin-service modifications

• Visibility of install base of serialized parts in service

• Ability to have access to engineering data related to in-service parts for technical support and documentation to customers



Digital Thread and Digital Twin Relationship� The Digital Thread that connects

the physical to the virtual product is data.

� The data can be comprised of: 3D Drawings

Part data; PN, SN, Dimensions, etc.

Part attributes; OEM Name, Life limits, Commodity codes, etc.

Performance data; max-min RPM, thrust, time

Operational data; operating time between removals, last repair, last removal, maintenance requirements, etc.

� PLM tools are evolving to enable the creation of digital twin to support: Manufacturing

Service Operations

The richness of the Digital Twin is the ability to take the Digital Thread and

model it using technologies like Big Data Analytics, Predictive Analytics and

Machine to Machine learning to make the virtual product operate just like the

physical product



Predictive Service follows a Capability Maturity Model



Capgemini Has the Partners and Tools to Enable the New Predictive World of Service



Problem Statement – I can’t predict when components will fail or ident ify rogue units in a timely manner.

“By implementing a micro-services architecture, show me how Capgemini can help me visualize, trend, identify and predict thousands of components on a fleet of aircraft?”

Problem Scenarios – Component Reliability

�An Aircraft component performance analysis system with a near future requirement of predictive analysis of components performance.

Key Problems Addressed

� Solution’s Phase 1 Problem : “ Identify rogue units in a timely manner.”Solution : Build dynamic User Interface(UI) screens which enable the performance tracking of aircraft components,

using Unit (component) based removal data.

� Solution’s Phase 2 : Problem : “ I can’t predict when components will fail ..!”Solution : Predictive analysis using IoT / machine learning to predict the failure of the components that enables cost

savings through increased performance and decreased maintenance cost and operational disruption.

Aviation Solution - Component Reliability System

Case Study - Aviation Component Reliability



Aviation Solution : Cloud Native Architecture



Aircraft Component Analysis UI (Splash Screen)

Aircraft Component Analysis UI :

�This UI will display top 10 worst performing components in terms of ATA, Company Serial Numbers, Tails(ACNs) and Company part Numbers using number of removals performed on the components and number of flight hours within last 30 days from current date or as defined.

�From any of the four graphs on the UI, user can ask for drilled down analysis of the displayed ATAs, CPNs, Company Sr. numbers and Tails. This will navigate user to “Component historical Analysis” UI.



Component Removal History UI

Component Removal History UI :

�Blue bars on the graphs indicate the duration for which the component has been in installed state on the aircraft while the spaces where blue bar breaks is when the component was removed from the aircraft.

�User can also see the details of all the removals performed on the component historically in the pop up screen such as: Installation and removal date, reason for removal, repair type, Installation/removal/repair location etc.



Unit Based Filter Editor Screen

Unit Based Filter Editor UI� Allows user to do specific searches with custom filters and be able to save those filters for reuse on a regular basis.

�User can select the component based on the attributes such as Fleet, Sub Fleet, Tail, ATA, CPN,MPN etc. and navigate to “Component Removal History” screen.

�User can also save the setting done for selecting the components or use earlier saved settings to select the components for historical analysis.



Parting Thought

“ If you went to bed last night as an industrial

company, you're going to wake up this morning as a

software and analytics company. The notion that there's

a huge separation between the industrial world and the

world of digitization, analytics and software is over.”

Jeff Immelt, CEO General Electric

The information contained in this presentation is proprietary.© 2016 Capgemini. All rights reserved.

www.capgemini.com

About CapgeminiWith more than 180,000 people in over 40 countries, Capgemini is one of the world's foremost providers of consulting, technology and outsourcing services. The Group reported 2015 global revenues of EUR 11.9 billion (about $13.2 billion USD at 2015 average rate) Together with its clients, Capgemini creates and delivers business, technology and digital solutions that fit their needs, enabling them to achieve innovation and competitiveness. A deeply multicultural organization, Capgemini has developed its own way of working, the Collaborative Business Experience™, and draws on Rightshore®, its worldwide delivery model. Learn more about us at www.capgemini.com.

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Predictive Maintenance & IoT a Capgemini POV