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CUSTOMER
SAP Predictive Maintenance and Service &SAP Asset Intelligence NetworkRyan Weicker,
Senior Support Engineer
Digital Business Services, SAP America
2CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ
Agenda
Asset Intelligence Network
• AIN Overview
• Functions and Features
• Integration
PdMS Overview
• Benefits Across the Maintenance Program
• PdMS Overview
• Asset Visualization
• Insight Providers
• Machine Learning Engine
PdMS Customer Example
3CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ
SAP Asset Intelligence Network
Bringing together Business partners
Operator
Manufacturer
Service
Provider
Regulator
SAP Asset Intelligence Network will provide a global registry of industrial equipment; built and shared between
multiple business partners and used across the industry by all stakeholders. This will enable new collaborative
business models resulting in true Operational Excellence.
Models /
Equipment
4CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ
Apps for collaborative processing e.g. equipment
lookup, announcements, service bulletins,
performance improvement, spare parts
management, obsolescence management
A cloud portal of standardized content that defines
and documents models and equipment, shared
and stored, for a consistent definition between
business partners.
A secure network to connect multiple business
partners for inter and intra company information
exchange and collaboration.
Apps
Content
Network
SAP Asset Intelligence Network
Enabling collaborative asset management
Combined together
to deliver
SAP Asset
Intelligence
Network
5CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ
Nameplate info
Service bulletins
Maintenance strategy
Spare Parts
Recalls
Bills of Materials
Designs and drawings
Sensor definition
Operating instructions
Maint instructions
Safety instructions
Product training
Failure modes
Design improvements
Measurement documents
Telemetry
Usage information
Installation information
Failure / incident data
Service bulletin receipt
Service bulletin processed
Risks and controls
Design recommendations
Manufacturer Operator
SAP Asset Intelligence Network
Collaboration between manufacturers, service provider, and operators
Netw
ork
Co
nte
nt
Ap
ps Job Instructions Announcements
Obsolescence
Management
Performance
ImprovementSpare Parts
Equipment as a
Service*
Work
Collaboration*
Commissioning &
Handover*
*planned
6CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ
Enable OEM and Operator collaboration using a Digital Twin
SAP Asset Intelligence Network
Model
Digital Twin
Physical Assets
Defined in SAP PM
OEM 1
Operator
20 MVA 3Phase
Transformer
20 MVA 3Phase
Transformer - 120 MVA 3Phase
Transformer - 220 MVA 3Phase
Transformer - 320 MVA 3Phase
Transformer - n
20 MVA 3Phase
Transformer - 120 MVA 3Phase
Transformer - 2
20 MVA 3Phase
Transformer - 320 MVA 3Phase
Transformer - n
OEM 2
OEM 3
7CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ
An internal Installed Base / Asset
Portal across multiple in-house
systems
Progressively benefit from an
expanding network of contributors
An Install Base / Asset Portal where
you invite key Service Providers and
Manufacturers
1
2
3
SAP Asset Intelligence Network
Building the network
8CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ
Reduce master data maintenance
effort
Reduce manual asset search effort
Receive notifications, service work
summaries and service bulletins
Establish one channel to many
manufacturer’s, EPCs and Service
providers
Push communication and alerts to
manufacturers / service providers
Lower asset life cycle costs
Enabler for self-regulation
Tracking of serialized components
being installed into a major
component (manufacturers orders
subcomponents)
Asset operator
OPERATORMANUFACTURER BUSINESS VALUE
Nameplate
Information
Maintenance
strategies / tasks
Manufacturer
#B
Service Provider
Manufacturer
#A
Manufacturer
#C
The SAP Asset Intelligence Network
Business value – Operator view
Documents and
Drawings
Spare Parts
Recommendations
Pump
Manufacturer AFlow Meter
Manufacturer BMotor
Manufacturer C
9CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ
The SAP Asset Intelligence Network
Business value - Manufacturer
Increase equipment
portal reachIncrease customer
lifetime value
Single source of truth /
system of engagement
Operator
#1
Operator
#2
Operator
#n
Maintain model (equipment)
information once
Get transparency into equipment
usage
Improve warranty and recall
processes
Specific customer commerce
Improve customer relationships
One solution for many customers
Basis for collaboration and future
business models
Offer additional services and
revenue
OPERATOR MANUFACTURER BUSINESS VALUE
Usage
information
Send &
receive data
Product &
service feedback
Specifications
& drawings
Recommendations
& updates
Manufacturer
10CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ
SAP Asset Intelligence Network
Applications
Admin Apps
Business Partners
Authorizations
Templates
Master Data Apps
Models
Equipment
Locations
Spare Parts
Documents
Instructions
Process Apps
Performance Improvement
Obsolescence Report
Error Code Lookup
11CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ
SAP Asset Intelligence Network
Equipment: Features
Information– Model Information
– Model Attributes
– Equipment Attributes
– Installation Information
– Life Cycle Information
Structure and Parts– Structure
– Spare Parts
Documentation– Model Documents
– Equipment Documents
– Instructions
– Announcements
Monitoring– Measuring Points
– Error Codes
– Improvement Cases
Time Line
12CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ
SAP Asset Intelligence NetworkContent - AttributesSAP Asset Intelligence Network
Content - Attributes
13CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ
SAP Asset Intelligence NetworkContent – Structure and Spare PartsSAP Asset Intelligence Network
Content – Structure and Spare Parts
14CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ
SAP Asset Intelligence NetworkContent – Integrated 3D VisualizationSAP Asset Intelligence Network
Content – Spare Parts using 3D Visualization
15CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ
SAP Asset Intelligence NetworkContent – 3D Work InstructionsSAP Asset Intelligence Network
Content – Work Instructions
16CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ
SAP Asset Intelligence NetworkContent – DocumentsSAP Asset Intelligence Network
Content – Documents
17CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ
SAP Asset Intelligence NetworkApplication – AnnouncementSAP Asset Intelligence Network
Application – Announcement
18CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ
SAP Asset Intelligence NetworkApplication – Measuring PointsSAP Asset Intelligence Network
Application – Measuring Points
20CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ
Asset Intelligence Network Model Information in SAP EAM (PM) Side Panel
The following information is
available in the side panel:
Model header information
Characteristics (Attributes)
Announcements
Instructions
Documents
21CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ
Integration to SAP EAM (PM)
View model information in PM side panel
SAP Asset Intelligence Network SAP ERP PM
Model Equipment*
Link Table
*could also be Functional Location depending on customer use
1
1) Find matching model in AIN
2) Link created
3) Option to create DMS documents from AIN Documents
2
Side Panel
View of Model
Info
requires SAP ERP 6.0 Enhancement Package 6 as a minimum
and use of SAP Business Client. Customer is required to
implement notes from SAP Service Market Place.
22CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ
SAP Asset Intelligence NetworkEAM (PM) Integration
Onboard PM Equipment / Functional Locations to AIN
– Ability to configure which remains as the master. Ability to configure
at each attribute level.
– Equipment structure, documents & attributes (characteristics) are
synchronized in a bi-directional way between PM and AIN
AIN Announcement processing on EAM
Manufacturer announcements processed by type
– POWL entry
– Work Item created for responsible user for Equipment per plant
– Notifications created
– Batch processing
23CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ
Use data out of SAP Asset Intelligence
Network directly into a MDG Change
Request (CR) e.g. equipment information is
validated, enriched in MDG before create or
change in SAP ERP.
From MDG CR search in AIN e.g. for
suitable model
Provided as part of standard MDG EAM
solution extension (by Utopia)
Integration with SAP Master Data Governance (MDG-EAM)
24CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ
Agenda
Asset Intelligence Network
• AIN Overview
• Functions and Features
• Integration
• Business Cases
PdMS Overview
• Benefits Across the Maintenance Program
• PdMS Overview
• Asset Visualization
• Insight Providers
• Machine Learning Engine
PdMS Customer Example
25CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ
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 of our solution is
to enable a data science
driven predictive
maintenance in order to
reduce unplanned failures
Run to Failure Preventative Predictive
*Use of Maintenance Strategy – Future
*Proportion of maintenance strategies are for illustration purposes only and will vary based on many factors
26CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ
Multiple Approaches to Predictive MaintenanceData science driven approaches are on the rise
Asset
Conditio
n
TimeTotal Failure
Functional FailureAudible Noise
Ancillary Damage
Battery Impedance Test
Hot to Touch
Potential Failure = First Indication of Failure
Human
Driven
T
F
Equipment
Driven
Data Science Driven
Oil Analysis
X-ray Radiography
P Potential Failure
Why now? IoT/device connectivity
Big data available for training models
Declining hardware and software costs
Massive computing powerP
P
P
More time to respond enables
greater flexibility to dynamically plan
maintenance events
27CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ
WHEELS & BRAKES
Energy Dissipation
versus Mileage
Replacements driven
by more logical
utilization rates
Installed
battery
=
Normal
battery
Data science based
health indicators
BATTERYBEARINGS
Vibration Analysis versus
Oil Analysis Program
Advanced condition
monitoring techniques
The Internet of Things Benefits the Entire Maintenance Program
On-Condition
Near real-time condition
monitoring
Preventive
Drive scheduled
maintenance based
on the right
utilization metric
Predictive – Leverage data
science-based health indicators
The Internet
of Things
improves existing
strategies
and enables
new data
science driven
maintenance
approaches
Run to Failure Preventive On-Condition Predictive
28CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ
Company
Owns and operates a
fleet of around
2,000 electro-trains,
2,000 locomotives
and 30,000 coaches
and wagons
Customer ExampleTrain Operator
28CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ
40% of maintenance is currently reactive
The maintenance strategy proportions are for illustration purposes only and not reflective of actual customer percentages
Run to Failure Preventive On-Condition Predictive*
*
29CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ
Solution
Customer ExampleTrain Operator
29CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ
• 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
Run to Failure Preventive On-Condition Predictive
30CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ
Agenda
Asset Intelligence Network
• AIN Overview
• Functions and Features
• Integration
• Business Cases
PdMS Overview
• Benefits Across the Maintenance Program
• PdMS Overview
• Asset Visualization
• Insight Providers
• Machine Learning Engine
PdMS Customer Example
31CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ
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
SAP Predictive Maintenance and ServiceDecision support across the ecosystem & asset lifecycle
DESIGN
BUILDSUPPORT
PURCHASE
OPERATE &
MAINTAINDISPOSE
Decision support to ALERT, DISCOVER AND REMEDY
Business DataMachine Data
Combining IT & OT data gives machine data context
32CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ
SAP Predictive Maintenance and ServiceSolution components and value drivers
Business DataMachine Data
SAP Leonardo Foundation
SAP Leonardo for Edge Computing
Business User
Domain Expert
Data Scientist
Data ManagerSAP Leonardo IoT Foundation
SAP Leonardo IoT Edge
Machine Learning Engine
Insight Provider Catalog
SAP Predictive Maintenance and Service
Asset Health
Control Center
Asset Health
Fact Sheet
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
33CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ
SAP Predictive Maintenance and ServiceSystem and component level visualizations
Machine Learning Engine
Insight Provider Catalog
SAP Predictive Maintenance and Service
Asset Health Control Center
Asset Health
Control Center
Asset Health
Fact Sheet
SAP Leonardo Foundation
SAP Leonardo for Edge Computing
SAP Leonardo Foundation
SAP Leonardo for Edge Computing
Logistics & Maintenance
Execution Systems
Business DataMachine Data
Asset Health Fact Sheet
34CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ
SAP Predictive Maintenance and ServiceInsight Provider Catalog
*”Health Status Overview” is an example of a custom Insight Provider built using SDK
35CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ
SAP Predictive Maintenance and ServiceAsset Health Control Center
Asset View
New Orleans Refinery
Houston Refinery
Asset View
Asset Health Control Center
36CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ
SAP Predictive Maintenance and ServiceAsset Health Control Center
Asset View
New Orleans Refinery
Houston Refinery
Asset View
Asset Hierarchy
Asset Hierarchy
Asset Health Control Center
37CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ
SAP Predictive Maintenance and ServiceAsset Health Control Center
Asset View
New Orleans Refinery
Houston Refinery
Asset View
Asset Hierarchy
Asset Hierarchy Insight Provider Catalog
Asset Health Control Center
Insight Provider Catalog
38CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ
SAP Predictive Maintenance and ServiceAsset Health Control Center
Asset View Asset Hierarchy
Asset Health Control Center
Insight Provider Catalog
Insight Provider Catalog
39CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ
SAP Predictive Maintenance and ServiceAsset Health Control Center
Asset View Asset Hierarchy
Asset Health Control Center
Insight Providers
Insight Provider Catalog Insight Providers
Insight Provider Catalog
40CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ
SAP Predictive Maintenance and ServiceAsset Health Control Center
Asset View Asset Hierarchy
Asset Health Control Center
Insight Providers
Insight Provider Catalog
Insight Provider Catalog
Insight Providers
Remaining
Useful Life
2
5
12
18
22
32
Remaining
Useful Life
2
10
5
8
13
16
41CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ
SAP Predictive Maintenance and ServiceAsset Health Control Center
Asset View Asset Hierarchy
Asset Health Control Center
Insight Providers
Insight Provider Catalog
Insight Provider Catalog
Insight Providers
42CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ
SAP Predictive Maintenance and ServiceAsset Health Control Center
Asset View Asset Hierarchy
Asset Health Control Center
Insight Providers
Insight Provider Catalog
Insight Provider Catalog
Insight Providers
43CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ
SAP Predictive Maintenance and ServiceAsset Health Control Center
Asset View Asset Hierarchy
Asset Health Control Center
Insight Providers
Insight Provider Catalog
Insight Provider Catalog
Insight Providers
44CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ
SAP Predictive Maintenance and ServiceAsset Health Control Center
Asset View Asset Hierarchy
Asset Health Control Center
Insight Providers
Insight Provider Catalog
Insight Provider Catalog
Insight Providers
*”Health Status Overview” is an example of a custom Insight Provider built using SDK
45CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ
SAP Predictive Maintenance and ServiceAsset Health Control Center
Asset View Asset Hierarchy
Asset Health Control Center
Insight Providers
Insight Provider Catalog
Insight Provider Catalog
Insight Providers
46CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ
SAP Predictive Maintenance and ServiceAsset Health Fact Sheet
Equipment View
Equipment View
Asset Heath Control Center
Asset Health
Control Center
Asset Health Fact Sheet
Serial #12345
Remain
ing
Useful
Life 2
5
1
2
1
8
2
2
3
2
47CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ
SAP Predictive Maintenance and ServiceAsset Health Fact Sheet
Equipment View
Equipment View
Asset Heath Control Center
Asset Health
Control Center
Asset Health Fact Sheet
Serial #12345
Remain
ing
Useful
Life 2
5
1
2
1
8
2
2
3
2
Insight Provider Catalog
Insight Provider Catalog
48CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ
SAP Predictive Maintenance and ServiceAsset Health Fact Sheet
Equipment View Asset Health
Control Center
Asset Health Fact Sheet
Insight Provider Catalog
Insight Provider Catalog
49CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ
SAP Predictive Maintenance and ServiceAsset Health Fact Sheet
Equipment View Asset Health
Control Center
Asset Health Fact Sheet
Insight Provider Catalog
Insight Provider Catalog Insight
Providers
Insight Providers
50CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ
SAP Predictive Maintenance and ServiceAsset Health Fact Sheet
Equipment View Asset Health
Control Center
Asset Health Fact Sheet
Insight Provider Catalog
Insight Provider Catalog Insight
Providers
Insight Providers
51CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ
SAP Predictive Maintenance and ServiceAsset Health Fact Sheet
Equipment View Asset Health
Control Center
Asset Health Fact Sheet
Insight Provider Catalog
Insight Provider Catalog Insight
Providers
Insight Providers
52CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ
SAP Predictive Maintenance and ServiceAsset Health Fact Sheet
Equipment View Asset Health
Control Center
Asset Health Fact Sheet
Insight Provider Catalog
Insight Provider Catalog Insight
Providers
Insight Providers
53CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ
SAP Predictive Maintenance and ServiceMachine 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
54CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ
SAP Predictive Maintenance and ServiceMachine Learning Engine
*PdMS 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
Failure Prediction
• 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
55CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ
Agenda
Asset Intelligence Network
• AIN Overview
• Functions and Features
• Integration
• Business Cases
PdMS Overview
• Benefits Across the Maintenance Program
• PdMS Overview
• Asset Visualization
• Insight Providers
• Machine Learning Engine
PdMS Customer Example
56CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ
Personas:
• Superintendent
• Maintenance planner
• Manufacturing Engineer
• Maintenance Technician
Goals:
• Monitoring health of connected assets
• Leveraging machine learning and telemetry-based
statistics to trigger automated ‘predictive’
notifications
• Improve availability and overall performance
Phases:
• #1: 1 plant | 1 critical asset
• #2: 1 plant | more assets
• #3 and beyond : more plants | more assets
Caterpillar - manufacturing division
Benefits:
• Reduce loss of production due to unplanned
downtimes
• Planned maintenance becomes dynamic –
responding to health signals and not to a fixed
schedule
• Reduce maintenance cost
57CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ
HANA
ERP
BODS PdMS R
PDMS HANA Server
• SAP HANA DB 1.0 SPS12 Revision 122 or higher
• SAP HANA Rules Framework (HRF)
• SAP XS Advanced (XSA) 1.0.34
• SAP PdMS On-Premise 1.0 FP02 + Patch 2
• SAP PdMS SDK
• RAM = 350 GB;
• Disk = ~400GB of Disk Space;
• OS RHEL 6.7 or higher
R Server:
• RAM = 16 GB;
• Disk = ~60 GB of Disk Space;
• OS RHEL 7.3
• R Version – 3.3.2
• Rserve Version – 1.8.5
Machine Learning Engine
Insight Provider Catalog
SAP Predictive Maintenance and Service
Asset Health Control
Center
Asset Health Fact
Sheet
Hana Rules Framework
Machine data
Maintenance
records
Automatic PM notifications
triggered by PdMS Alerts
using oData
ML
models
and
scores
HRFRule services
and rules
Gateway
Hub
SLT
Solution components & PdMS microservices
58CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ
Machine Learning
Principal Component Analysis:
• PdMS READINGS table to be populated with
raw data; outliers removal
• Running instructions data, Time series data.
• Align time series and impute data. Pivot view
of all sensors. Aggregate views
• Training & Scoring
• Scheduling
Weibull Analysis:
• Workorder/notification data from maintenance
system.
• Fetch required info from PdMS EVENTS table
as a dynamic view.
• Project scoring view for next X units of time.
• Train model based on dynamic input view.
• Schedule training & scoring on a periodic
basis.
• User will always have a projection for next X
units of time based on all analysis of work
order data.
Rules
Telemetry Rules:
• Driven by sensor upper and lower
bound limits
• Driven by direct machine alerts
severity
Machine Learning Scores rules:
• Driven by anomaly scores above
severe and critical thresholds
• Driven by probability of failure above
severe and critical thresholds
Maintenance records rules:
• PM notifications not attended to
during past X units of time
• PM work orders not attended to
during past X units of time
Data Integration
• Fully automated initial/ delta loads from
OT/IT
• Frequency of delta loads managed by job
scheduler.
• Control table mechanism to manage
individual Machine data system API
modules.
• Job logs to capture processing history of
data extraction/load run.
• Emails notifications in case of job failure.
• Plant Maintenance notifications created in
backend ERP system using OData via
SAP Gateway Hub system.
PdMS @ CAT – A Dynamic System
59CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ
Ingest raw data Align sensor time
series
Impute sensor
values
Train model ** Score model
Filter timestamps
where asset is not
operating
[ data for
training ]
[ data for
scoring ]
Apply HRF
rules
[data sci. scores]
Create PdMS
Alerts
Trigger HRF
Actions
BODS
XSA scheduler
PdMS Executor[ model ]
[ equidistant-imputed-
filtered-pivoted table ]
Remove duplicates
and outliers*
Load delta data
XSC scheduler
[ rule service view ]
[ rest end point ]
Create PM
notification
[ call .xsjslib ]
[ telemetry/sensors data ][ machine data * ]
[ SAP PM data ]
Pivot data
** outliers are removed only for freedom elog data
** model training uses different job execution frequency than model scoring
Continuous Data processing – Scheduling
Thank you.
61CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ
SAP Leonardo IoT - The Big Picture
PEOPLEUI Layer (SAP Leonardo Bridge)
Enterprise Management – The Digital Core
Things / Physical Layer
PRODUCTS
“THINGS”
Procurement
R&D
Supply
Chain
Planning
Manufacturing
Logistics
Sales
After Sales
Service
PROCESSES
SAP Leonardo IoT Foundation
SAP Cloud Platform / SAP HANA Platform
SAP Leonardo
IoT Edge
SAP Leonardo IoT Apps
Connected Goods Connected Mfg.Track & Trace Vehicle Insights Predictive Main. Asset Intelligence NetworkNetwork Log. Hub
PLATFORM
APPLICATIONS
62CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ
PdMS Solution Architecture
Safety Control
Plant Databases
DCS PLC/SCADA
Devices
Advance Control
Manual Data
Device
Connectivity
Device
Management
Portal
Data Ingestion
Ingestion Pipeline
Landing Zone
Batch Stream
Files Messages
Transformations
Rules
TimeSeries Database
Exploration Zone
Data Fusion
Key Figures
Rules
Predictive ModelsExtensions
ERP/CRM
Data Archive
Production Zone
Data Fusion
Key Figures
Rules
Predictive Models
ERP / CRM
Business process integration
Transport
Derived
Signals
PdMS Application
Insight
ProviderInsight
ProviderInsight
Provider
On-demand
replication
Real-time
replication
63CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ
PdMS Solution Architecture
Safety Control
Plant Databases
DCS PLC/SCADA
Devices
Advance Control
Manual Data
Device
Connectivity
Device
Management
Portal
Data Ingestion
Ingestion Pipeline
Landing Zone
Batch Stream
Files Message
s
Transformations
Rules
TimeSeries Database
Exploration Zone
Data Fusion
Key Figures
Rules
Predictive ModelsExtensions
ERP/CRM
Data Archive
Production Zone
Data Fusion
Key Figures
Rules
Predictive Models
ERP / CRM
Business process integration
Transport
Derived
Signals
PdMS Application
Insight
ProviderInsight
ProviderInsight
Provider
On-demand
replication
Real-time
replication* Planned
SAP DATA SERVICES
TELIT, SAP PCo,
IoT SERVICES*
BIG DATA HUB*
HANA SMART
DATA STREAMING
SAP IQ, OSISoft PI,
HADOOP/VORA*
SAP HANA &
HANA RULES
FRAMEWORK
R
SAP HANA &
HANA RULES
FRAMEWORK
R
UI5 &
XSA
ODATA / HCI /
SAP PO*