Sap PdMS Predictive Maintenance Service

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SAP Predictive Maintenance and Service, on premise edition

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release any functionality mentioned therein. This document, or any related presentation and SAP’s strategy and possible future
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All forward-looking statements are subject to various risks and uncertainties that could cause actual results to differ materially
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Legal disclaimer

Introducing the SAP Leonardo IoT Portfolio
Benefits Across the Maintenance Program
SAP Predictive Maintenance and Service
▪ Overview
▪ Asset Visualization
• Analysis Tools Catalog
• Explorer
• Details
▪ Machine Learning Engine
Agenda

▪ Overview
• Explorer
• Details

Intelligently connecting People, Things and Processes
Artificial Intelligence and
Machine Learning
Business Process
Integration
APIs &
Microservices
Natural
Language
Assets &
Machines
Big Data
Business
Applications
Business ModelsSocial &
Collaboration

SAP Leonardo IoT Bridge
Product Insights
Goods and Equipment
Supply Networks
Fixed Asset Insights
Manufacturing
Execution
Manufacturing
Networks
Mobile Asset Insights
Logistics Safety
Logistics Networks
Building Insights
Construction
Energy Grids
Market Insights
Rural Areas
Urban Areas
People and Work
People and Health
People and Homes
Connected
Products
Connected
Assets
Connected
Fleet
Connected
Infrastructure
Connected
Markets
Connected
People
SAP Leonardo IoT Foundation
SAP Cloud Platform / SAP HANA Platform
SAP Leonardo
IoT Edge
Things / Physical Layer
Enterprise Management – The Digital Core

Connected Assets – Fixed Asset Insights
Optimize asset performance throughout the entire lifecycle
and build an asset network collaboration for better service
and maintenance process
As companies move from a reactive to a proactive approach to maintenance,
fixed asset insights provides an end-to-end solution for predictive maintenance
and service from identification of emerging issues to procuring spare parts,
scheduling and executing maintenance. These capabilities can serve assets
that are both owned and operated by companies as well as assets that are
installed at a customer site and are covered by service contracts. This can be
achieved through an integrated asset network to collaborate between
manufacturers, service providers and end customers.
SOLUTION SPOTLIGHT
• SAP Predictive
Maintenance and Service
• SAP Asset Intelligence
Network
• SAP Plant Maintenance

▪ Overview
• Explorer
• Details
Customers
Roadmap

Multiple Approaches to Predictive Maintenance
IT driven approaches are on the rise
AssetCondition
Time
Total Failure
Functional Failure
Audible Noise
Ancillary Damage
Battery Impedance Test
Hot to Touch
Potential Failure = First Indication of Failure
Human
Driven
T
F
Equipment
Driven
IT (data science & rules) Driven
Oil Analysis
X-ray Radiography
P Potential Failure
Why more IT driven approaches?
▪ IIoT/device connectivity
▪ Big data available for training models
▪ Declining hardware and software costs
▪ Massive computing power
P
P
P
More time to respond enables
greater flexibility to dynamically plan
maintenance events

Technology is changing our approach to maintenance
Run to Failure Preventative Predictive
*Use of Maintenance Strategy – Today
The Internet of Things is leading to
increased use of predictive
maintenance
Although still relevant,
preventative maintenance
typically results in over-maintaining
assets and high cost
The goal is to enable more
IT driven (data science &
rules driven) approaches to
predictive maintenance in
order to reduce unplanned
failures and the number of
overall maintenance actions
*Use of Maintenance Strategy – Future
*Proportion of maintenance strategies are for illustration purposes only and will vary based on many factors

Asset Health Distribution
Two areas of focus drive business value
2. Dynamic decision making for
the healthy population of
assets
1. Alerting for rare
events
Healthy Unhealthy
Greatest ROI potential

WHEELS & BRAKES
Energy Dissipation
versus Mileage
Replacements driven
by more logical
utilization rates
Installed
battery
=
Normal
battery
Data science driven alerting and health
indicators
BATTERYBEARINGS
Rules driven alerting and
health indicators
The Internet of Things Benefits the Entire Maintenance Program
Predictive – Rules driven
Automated and real-time
condition monitoring based
on rules engine
Preventive
Drive scheduled
maintenance based
on the right
utilization metric
Predictive - Data Science Driven
Automated and real-time condition
monitoring based on machine
learning
The Internet
of Things
improves existing
strategies
and enables
new data
science & rules
driven
approaches to
maintenance

Company
Owns and operates a
fleet of around
2,000 electro-trains,
2,000 locomotives
and 30,000 coaches
and wagons
Customer Example
Train Operator
40%of maintenance is currently reactive
The maintenance strategy proportions are for illustration purposes only and not reflective of actual customer percentages
*
*

Solution
Customer Example
Train Operator
• Improve effectiveness
of maintenance
programs
• Data fusion between
IT and OT data
• Remote train
diagnostics
• Engineering rules and
predictive models
• Dynamic planning of
maintenance schedules
BRAKES
Energy Dissipation
versus Mileage
DOORS
Open/Closure Cycles &
Times
versus Mileage
• Higher asset availability & passenger satisfaction
• Projecting 100M Euro savings per year in
maintenance operations costs when fully
implemented
Benefits
Improved
Program
Effectiveness
Starting with
Improvements
to Preventative
Maintenance
Plans

Service
Service Provider
Sales
Increase
customer
satisfaction
and loyalty
Dealer
Deliver the
value added
service at the
right price
Fleet
Owner/Operator
Decrease
maintenance
costs
Operator
Increase
asset up-time
R&D
Improve
asset
reliability
and up-time
Monitor
quality of
purchased
components
Improve
manufacturing
processes
Comply
with contract
service level
agreements
AftermarketProcurement Production
OEM
Decision support across the ecosystem & asset lifecycle
DESIGN
BUILDSUPPORT
PURCHASE
OPERATE &
MAINTAIN
DISPOSE
Decision support to ALERT, DISCOVER AND REMEDY
Business DataMachine Data
Combining IT & OT data gives machine data context

Decision support across the ecosystem & asset lifecycle
DESIGN
BUILDSUPPORT
PURCHASE
OPERATE &
MAINTAIN
DISPOSE
R&D AftermarketProcurement Production
OEM
Service
Service Provider
Sales
Dealer
Fleet
Owner/Operator
Operator
Reduced
warranty cost
Service, manufacturing and
design engineers can
reduce warranty costs
through increased visibility
of fielded assets.
New
business
models
Improved
service
profitability
OEMs are now able to
offer new service
business models such
as full-service
agreements or pay-
per-use.
OEMs and 3rd party
service providers can
offer high margin services
with reduced risk through
increased visibility of
fielded assets.
Reduced
maintenance
cost
Maintenance schedules
can now be dynamically
planned and packaged to
better utilize resources
and scheduled asset
downtime.
Increased
asset
availability
With the use of data
science, asset operators
or service providers can
predict failures early and
implement corrective
actions to avoid
unplanned downtime.
Increased
first-visit fix
rate
Service Managers can
identify the required
skills and spare parts
prior to arriving at the
job site.

Solution components and value drivers
SAP Leonardo Foundation
SAP Leonardo for Edge Computing
Business User
Domain Expert
Data Scientist
Data Manager
SAP Leonardo IoT Foundation
SAP Leonardo IoT Edge
Machine Learning Engine
Analysis Tools Catalog
Explorer Details
Logistics & Maintenance
Execution SystemsActions
Insights
Alerts
Raw
Data
 Enables a data science driven
approach to condition monitoring
 Flexible extension concept for
customers to build industry or
customer specific models and
analytics
 A scalable Machine Learning
Engine that drives data science
insights into our business
processes
 Flexible visualizations across
equipment structures
 End-to-end process integration…
Alert, Discover, Remedy

System and component level visualizations
Explorer
Explorer Details
Execution Systems
Details

*”Health Status Overview” is an example of a custom Analysis Tool built using SDK

Explorer
Locations
New Orleans Refinery
Eagle Ford Field
Locations
Explorer

Explorer
Locations
Eagle Ford Field
Locations Filter by Location
Filter by Locations
Explorer

Explorer
Locations
Eagle Ford Field
Locations
Filter by Locations
Filter by Location Analysis Tools Catalog
Explorer

Explorer
Locations Filter by Location Analysis Tools Catalog
Explorer

Explorer
Key Figures Analysis Tool
Analysis Tools Catalog Analysis Tool
Explorer

Explorer
Equipment List Analysis Tool
Analysis Tool
Explorer

Explorer
Locations Filter by Location Analysis Tools Catalog
Analysis Tool
Explorer
Equipment Scores Analysis Tool

Explorer
Map Analysis Tool
Analysis Tool
Explorer

Explorer
3D Chart Analysis Tool
Analysis Tool
Explorer

Explorer
Custom Analysis Tool
Analysis Tool
*”Health Status Overview” is an example of a custom Analysis Tool built using SDK
Explorer

Details
Equipment View Explorer
Explorer
Equipment View
Serial #12345
Details

Details
Explorer
Details
Equipment View
Serial #12345

Details
Details

Details
Alerts Analysis Tool
Details

Details
2D Chart Analysis Tool
Details

Details
Work Activities Analysis Tool
Details

Machine learning challenges
High dimensional data
No labeled failure data
Rare failure events
Outdated models, human scale
Use case specific algorithms
Feature construction/selection requires data
scientists & domain user collaboration
Model management, continuous learning and scoring
Anomaly detection and reinforcement
through user feedback
Failure prediction using ensemble learning
Extensibility and integration of new algorithms
SOLUTION

Explorer Details
Execution Systems
*Roadmap Item
Continuous Improvement & Learning
Failure
Prediction
Trigger prediction when
algorithm detects a
specific combination of
input variables
Anomaly Detection
Trigger anomaly alert
when the algorithm
detects an abnormal
pattern
New
Algorithms
Extensibility
Model
Management
Tools
Reinforcement*
Domain expert
feedback

*Roadmap Item
Continuous Improvement & Learning
Failure
Prediction
Trigger prediction when
algorithm detects a
specific combination of
input variables
Anomaly Detection
Trigger anomaly alert
when the algorithm
detects an abnormal
pattern
New
Algorithms
Extensibility
Model
Management
Tools
Reinforcement*
Domain expert
feedback
• Supervised learning enables failure
predictions like Remaining Useful Life
• Finds contributing factors to failure events
• Unsupervised learning detects anomalies
• Enables Health Scores
• Expert feedback
• Models change as operational
environment changes
• Extensibility for out-of-the-box
algorithms
• Possibilities to deploy new
R based algorithms

Machine Learning Engine - Model management
• Machine learning models are automatically applied to new incoming data
• Models are regularly re-trained using scheduling capabilities
• Model management capabilities allows us to maintain model versions
Configure model Score model
Deactivate
Train model
Retrain model
Model
Configuration
Model Version Scores

Extending the machine learning engine
4. Configure MLE3. Maintain Metadata2. Install Package1. Write R Package
Start using the new algorithm for model creation, training and scoring!
• Wrap custom
algorithm in R
package
• Export training and
scoring function
• Install the package on
the R Server
• Create metadata for
health score created by
the new algorithm
PropertySetType
PropertyName1::Type
PropertyName2::Type
PropertyName3::Type
• Register the package at the MLE

Operational Data Ingestion with SAP
Integration with SAP MII and PCo
MII = Manufacturing Intelligence and Integration
PCo = Plant Connectivity
SAP MII
SAP PCo
Data
Historians
Safety Control
Plant Databases
DCS PLC/SCADA
Devices
Advance Control
Manual Data
Real
Time
SAP Predictive
Maintenance and Service
SAP Data
Services
Offline files
Batch
On-demand,
Near
Realtime
e.g. OPC

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set forth in the express warranty statements accompanying such products and services, if any. Nothing herein should be construed as constituting an additional warranty.
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any functionality mentioned therein. This document, or any related presentation, and SAP SE’s or its affiliated companies’ strategy and possible future developments, products,
and/or platform directions and functionality are all subject to change and may be changed by SAP SE or its affiliated companies at any time for any reason without notice. The
information in this document is not a commitment, promise, or legal obligation to deliver any material, code, or functionality. All forward-looking statements are subject to various
risks and uncertainties that could cause actual results to differ materially from expectations. Readers are cautioned not to place undue reliance on these forward-looking statements,
and they should not be relied upon in making purchasing decisions.
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