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© 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
Te
ch
nic
al &
Im
ple
me
nta
tio
n C
om
ple
xit
y
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
tio
n C
om
ple
xit
y
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 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