© 2016 IBM Corporation

Predictive Maintenance

Daniele Pietropaoli

Predictive Analytics Solutions Specialist

© 2016 IBM Corporation 2

Agenda

La Predictive per il Maintenance in Azienda

IBM Analytics Application

Target di analisi

Patrimonio informativo

Road map evolutiva del Maintenance

Metodologia & Architettura

Front end della Piattaforma analitica

Best Practices

© 2016 IBM Corporation 3

IBM Analytics

Predictive Operational Analytics

Manage Maintain Maximize

Predictive Fraud Analytics

Monitor Detect Control

Predictive Customer Analytics

Acquire Grow Retain

© 2016 IBM Corporation 4

IBM Analytics

Predictive Operational Analytics

Manage Maintain Maximize

© 2016 IBM Corporation 5

Monitorare, mantenere ed ottimizzare i propri assets

Prevedere probabili malfunzionamenti degli impianti, dei

macchinari e dei beni di funzionamento aziendale.

Analisi degli scenari tramite analisi di predictive

maintenance e analisi dei processi con what if analysis

Programmare la manutenzione degli assets per

diminuire lo “spare parts management” della catena di

produzione degli impianti.

IBM Predictive Maintenance per la riduzione dei costi operativi, il

miglioramento degli asset di produzione e l’incremento dell’efficienza

© 2016 IBM Corporation 6

Identificare i dati di analisi

Braccio idraulico si

rompe a causa di guasto

nella parte 7097: valvola

di sequenza (errore

79012).

La produzione

si ferma

Textual Data

• User’s report

• Usage report

• Maintenance Report

Failure

Type &

date

Wearout data

• Latest usage (duration,

type)

• Usage Conditions

(weather, urgency..)

Maintenance data

• Latest maintenance Date

and type

• Spare parts age

• Spare parts providers

Operational data

• Temperatures

• Flow

• Pressions

• Vibrations

• Tensions ….

© 2016 IBM Corporation 7

Maintenance Landscape

Reactive

Prescriptive

Predictive

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tio

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xit

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Maintenance Model

Time Based

Maintenance

Condition Based

Maintenance

Reactive

Maintenance

Proactive

Maintenance

Current Status

of Industry

Future Status

of Industry

Maintenance Model

Te

ch

nic

al &

Im

ple

me

nta

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om

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Maintenance Model

© 2016 IBM Corporation 8

La Predictive Maintenance analizza i dati provenienti da più fonti e suggerisce le

migliori azioni consentendo di prendere decisioni tempestive

Asset Maintenance Asset Performance Process Integration

Raccogliere ed

integrare i dati

Generare modelli

statistici e predittivi

Visualizzare avvisi ed azioni

consigliate a supporto Piattaforma integrata

degli eventi futuri

Predictive

Maintenance

Methodology

1

2

3

4

Analisi sulle cause degli eventi

5

© 2016 IBM Corporation 9

So what’s different about our approach?....

Process Management &

Control Dashboarding from Maintenance Mgt System and distribution of predictive analytics results

Predictive

Reporting

Actionable Insights

Actionable Insights

Process

Automation &

Optimization

Automate prediction &

deployment process

Predictive Analytics Platform

Analytics for ‘through the windscreen’ view .

Predictive insights improve Management data and refine business rules

© 2016 IBM Corporation 10

Asset Analyst – Model Predicted Failure & Correlation

© 2016 IBM Corporation 11

Application :

© 2016 IBM Corporation 12

Best Practices :

© 2016 IBM Corporation 13

An oil and gas producer in Australia uses predictive modeling

to anticipate and avoid costly equipment failures

87% accuracy and 48-hour warning about

potential equipment failures

Solution components

Business challenge: This oil and gas exploration and production company in

Australia was struggling to meet production targets because equipment

failures would arise out of the blue, bringing drilling to a halt at sites that were

several days’ drive from the nearest city. It needed a way to increase

equipment reliability, minimize downtime and maximize production.

The smarter solution: The company built an analytics solution that uses

predictive modeling to identify situations that can lead to equipment outages

so that it can dispatch repair units proactively. Drawing upon current and

historical data, the solution also provides business users with accurate

insight into production potential so that they can create better forecasts and

make smarter purchasing decisions.

In this industry, reliable equipment equals steady production, which is what

drives profit. Being able to make repairs before they impact production has

had a significant positive impact on the company’s bottom line.

Integrates four existing databases

and keeps them relevant

• IBM® SPSS® Modeler Desktop

• IBM SPSS Modeler Server

• IBM SPSS Statistics Desktop

• IBM SPSS Training

• IBM SPSS Lab Services

Millions of dollars saved thanks to better

insight into purchasing

decisions

© 2016 IBM Corporation 14

Honda R&D Co., Ltd. uses predictive analytics to improve the

performance and safety of its electric vehicle batteries

50% reduction in CO2 emissions by commercializing EV technology

Business Challenge: Because all-electric vehicles (EVs) do not use gasoline like

traditional or hybrid cars, they rely entirely on their batteries for power. Honda

R&D Co., Ltd., a division of Honda Motor Co., Ltd., wanted to better understand

what factors had the greatest impact on battery performance and longevity.

The Smarter Solution: Honda R&D can now gather and analyze near-real-time

battery data from FIT EV on the road in Japan and the United States. Analysis

can identify which operating factors, such as road conditions, charging patterns

and trip length, have the greatest impact on battery life. Further analysis can

help the automaker predict when batteries need replacing, so it can alert owners

in advance.

“Data gathered from the real-world operation of our vehicles is critical to predict

the longevity of current batteries and greatly influences future product design.”

—Senior Chief Engineer, Automobile R&D Center,

Boosts

confidence and customer satisfaction

with EVs by improving

performance

Improves design by analyzing massive

amounts of operating data

Solution Components

• IBM SPSS Modeler Desktop

• IBM SPSS Modeler Server

• IBM Global Business

Services

© 2016 IBM Corporation 15

Shandong Luneng Software Technologies Ltd. helps electric power

providers reduce maintenance costs and optimize parts inventory

Reduce expensive damage resulting resulting from unexpected breakdown

Optimize procurement model of major equipment and warehouse management of spare parts

Save approximately 400M ¥ Yuan (US$65M) of maintenance costs in 5 years Challenge: Provider of information services and systems to the

equipment-intensive enterprises electric power industry sought more

effective management of clients’ capital equipment and production

assets. Specifically wanted to reduce unplanned shutdown time,

control and lower the operating costs, optimize operating plans, and

improve poorly integrated maintenance systems.

Solution: IBM PMQ Predictive Maintenance and Quality to accurately

predict the equipment failure, optimize the warehouse management

of spare parts, lower procurement costs, and improve efficiency of

equipment utilization.

• IBM PMQ Predictive

Maintenance and Quality

• Solutions and services from

IBM Business Partner Genius

Systems Ltd

Solution Components

© 2016 IBM Corporation 16

Australian mining company Thiess Pty. Ltd. uses predictive

analytics to improve equipment availability and reliability

Improves mining equipment

availability and uptime

Increases revenue and production

efficiency

Reduces maintenance downtime,

parts inventory and costs

Challenge: Thiess needed the ability to collect, analyze and use the

equipment sensor and other data already available to it to improve

preventive maintenance and to predict and avoid equipment downtime.

Solution: Thiess is planning to pilot a predictive machine management

solution that not only analyzes the current condition of mining equipment

but also predicts machine health far enough in the future to enable decision

makers to execute corrective actions such as adjusting production plans or

ordering spare parts to avoid failure.

“Building a platform that feeds the models with the data we collect and then

presents decision support information to our folks in the field will allow us to

increase machine reliability, lower energy costs and emissions, and improve

the overall efficiency and effectiveness of our business.”

— Ben Willey, mining technology and innovation manager

Solution Components

• IBM SPSS Modeler Server

• IBM SPSS Statistics Server

• IBM DB2

• IBM Global Business

Services

© 2016 IBM Corporation 17

Question & Answer