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1101001
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Thomas Klyvø
Senior Solution Engagement Manager
Products and Innovation – SAP SE
October 2016
SAP Predictive Maintenance and Service
© 2015 SAP SE or an SAP affiliate company. All rights reserved. 2 Customer
Legal Disclaimer
The information in this document is confidential and proprietary to SAP and may not be disclosed without the permission of SAP. This presentation is not subject to your license agreement or any other service or subscription agreement with SAP. SAP has no obligation to pursue any course of business outlined in this document or any related presentation, or to develop or release any functionality mentioned therein. This document, or any related presentation about SAP’s strategy and possible future developments, directions, and functionality of products and/or platforms, are all subject to change and may be changed by SAP 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. This document is provided without a warranty of any kind, either express or implied, including but not limited to, the implied warranties of merchantability, fitness for a particular purpose, or non-infringement. This document is for informational purposes and may not be incorporated into a contract. SAP assumes no responsibility for errors or omissions in this document, except if such damages were caused by SAP intentionally or grossly negligent.
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, which speak only as of their dates, and they should not be relied upon in making purchasing decisions.
© 2015 SAP SE or an SAP affiliate company. All rights reserved. 3 Customer
• Establish an overview of SAPs solution and vision on Predictive Maintenance & Services (PdMS)
• Understand the journey – Running a predictive maintenance project requires huge efforts but wields equally big potentials
• Case of Trenitalia
Purpose of today’s session
*) OT = operational technology
© 2015 SAP SE or an SAP affiliate company. All rights reserved. 4 Customer
Insight Action
IT/OT* Convergence
• Big Data ingestion
• Big Data infrastructure
• Merging sensor data
with business
information
Maintenance activities
• Prioritized maintenance
and service activities
• Optimized warranty
and spare parts
management
• Prescriptive
Maintenance
• Quality improvements
Data analysis
•Root cause analysis
•Asset health monitoring
•Machine learning
•Anomaly detection
• Triggering of corrective
actions
Connected assets
• Onboarding
• Connectivity
• Device management
• Security
Business Value
• Customer experience
• Increased quality
• Lower costs
• Operational efficiency
• R&D effectiveness
• Material procurement
Sensor Data Insight Action Outcome
SAP Predictive Maintenance and Service solution From sensor to outcome
*) OT = operational technology
© 2015 SAP SE or an SAP affiliate company. All rights reserved. 5 Customer
OT
connectivity
Big Data platform: SAP HANA Platform
IT
integration
5 Public
Operationalized Analytics & Data Science Services
Connected
assets
Process
automation
SAP Predictive Maintenance and Service
Conceptual Architecture
Business
systems
(SAP S/4HANA,
SAP CRM,
and so on)
Devices,
machines,
and
sensors
IoT
base services IT
connectivity
Operational analytics
Application
Extensions
OT (device)
integration
© 2015 SAP SE or an SAP affiliate company. All rights reserved. 6 Customer
Data Management
Data Fusion
Data Processing
Actions
Raw Data
Insights
Data Manager
Data Scientist /
Data Analyst
Domain Expert
Business User /
Operational User
Operational System, e.g. PM, CRM, MRS, etc.
Application
Insight Providers
(micro-services, operationalized analytics)
Derived Signals
… cu
sto
m
cu
sto
m
cu
sto
m
SAP Products and Capabilities How SAP Approaches Predictive Maintenance and Service
Capture operations
technology (OT) data through
scalable and cost-effective
process
Translate the OT data into
consumable signals
Deliver insights to the domain
expert from interpreted
signals
Take specific actions utilizing
existing business systems
© 2015 SAP SE or an SAP affiliate company. All rights reserved. 7 Customer
Predictive Maintenance and Service On-Premise Edition (PdMS OPE)
Business Applications built on Modular Analytics
Geo-
Spatial
Insight
Provider
Asset
Explorer
Insight
Provider
Key
Figure
Insight
Provider
3D
Visualizat
ion
Insight
Provider
Predictive Maintenance and Service
Insight Provider
Lifecycle Management
Data Science Modeling &
Scoring
Insight Provider Runtime Services
Work
Activities
Insight
Provider
Derived
Signal
Insight
Provider
SAP HANA Enterprise
Edition
SAP IQ
SAP Data Services*
SAP ESP*
SAP Predictive Analysis*
SAP Lumira*
*Optional components
Asset Health Control Center (AHCC)
Additional
Custom
Insight
Provider
Predictive Maintenance and Service On-Premise Edition
Connected
Assets
Devices,
machines,
sensors
Integration
possible with
Telit
DeviceWise,
SAP PCo
Process
Automation
Closed-loop
business
process
integration
into PM and
MRS
Process
Integration
Data Management
Remaining
Useful Life
Prediction
Distance-
Based
Failure
Analysis
Anomaly Detection with Principal Component Analysis
Data Science Services Insight Provider
Product
Integration
IoT
Ap
pli
cati
on
s
Op
era
tio
nali
zed
An
aly
tic
s a
nd
Data
Scie
nce
Serv
ices
IoT
Base
Serv
ices
Big
Data
Pla
tfo
rm
Asset Health Fact Sheet (AHFS)
© 2015 SAP SE or an SAP affiliate company. All rights reserved. 8 Customer
Health status at a glance
Health status of the complete fleet
Aggregated from component health scores
Based on out-of-the-box machine learning
Derived Signals Management
Personal and extensible
Flexibly composed by insight providers
Drill-down into 1 machine
Integrated into operational processes
E.g. close-loop integration for services
via Multi-Resource Scheduling (MRS)
SAP Products and Capabilities Asset Health Control Center: Supervise Machine Health and Act on Issues
Maintenance Innovation in Context
Railway Case
© 2015 SAP SE or an SAP affiliate company. All rights reserved. 10 Customer
Trenitalia at a Glance
Fully owned by the FS Group, 100% controlled by the
Ministry of Treasury
Staff 31.082 employees
Fleet 1.580 locos
112 EMUs (electro-trains)
1.547 MUs (light trains)
25.896 coaches and freight cars
498 shunting locos
Passengers 38,6 Bn of passenger / km per year
Tons 14,7 Bn of tons / km per year
Trains 7.263 / day
Revenue 5.577 M€
© 2015 SAP SE or an SAP affiliate company. All rights reserved. 11 Customer
The Business Value of Maintenance Innovation
Perform ALL the required interventions, and ONLY the required interventions, at the RIGHT TIME, ensuring
availability of the RIGHT RESOURCES
Prevent breakdowns while trains are in operations
Prevent extended maintenance downtime due to unforeseen activities
Reduce Unplanned Downtime
Avoid any unnecessary activity
Plan in advance and in detail for any intervention, ensuring availability of spare parts, facilities, tools and trained resources
Reduce Costs of
Operations
>8-10% of direct savings on impacted maintenance costs
>5-8% increase in asset availability
>x% of reduction of breakdowns of trains in operations
Ground diagnostic
On board diagnostic
Dynamic Maintenance Management System
© 2015 SAP SE or an SAP affiliate company. All rights reserved. 12 Customer
The Problem with Preventative Maintenance Planning
10K 20K 30K 40K 50K 60K 70K 80K 90K 100K
Depth of Maintenance Activity
• Work done by simple assets can be described by a simple indicator such as time of operations or units of throughput, but if we consider a complex machine like a train, the specific operational conditions have a huge impact on how its various components wear out
• These operational conditions are not known a priori by the manufacturer of the machine, and can change dramatically in the day-by-day work – this creates the need of operating under extremely conservative assumptions
• The only real value of these plans is that they are logistically very simple to execute and don’t require much information
© 2015 SAP SE or an SAP affiliate company. All rights reserved. 13 Customer
Increase Planning Efficiency and Relevance
With Life and Health Indicators
Life
Measure as precisely as possible the expected wear of the part by counting and projecting a set of relevant parameter (e.g. cycles, hours of operation, kilometers, energy etc.)
Maintenance is performed when predefined thresholds are reached
Health
Takes into account the actual status of operation by measuring physical parameters (e.g. closing time for a door, temperatures of cooling systems) or relevant combinations of indicators
Maintenance is performed when the parameter goes out of the normal range, indicating a probable deterioration of condition
© 2015 SAP SE or an SAP affiliate company. All rights reserved. 14 Customer
Planning Engine
Improving the Relevance of Maintenance Activities
Planning based on Km / Time of operations
Planning based on Life Indicators
Planning based on Life and Health Indicators
• Current model, standard in the industry
• Easy to operate because all the components in the material share the same driver
• Sub-optimal for the same reason
• Based on more relevant drivers and indicators that better represent the effective current and expected usage of every single component
• Increased precision is directly connected with the quality and precision of the planning for the materials
• Requires optimization methods in order to produce a plan, due to the fact that every component in a material can have different life situation
• Further increase the relevance of the plan, considering the future effects that the evolution of life indicators will have on the ability of every component to perform, and on its risk of failure
• Requires sophisticated mathematical methods to predict the behavioral patterns of health indicator based on the expected usage of the materials
• Health indicators can in any case used to trigger short term maintenance activities
© 2015 SAP SE or an SAP affiliate company. All rights reserved. 15 Customer
Life and Health Indicators in Action
Bearings Wheels Engine Doors Pantograph A/C …
Life Ind. f(Km, radios, weight) f(Km, exchanges, radios, weight) f(Km, weight) Open / close cycles Up / down cycles f(hours, ext temp, int temp)
Health Ind. Vibration pattern Edge shape f(power gen, power absrbd) Open / close time Up / down time f(Delta int temp)
Telemetric readings
Operational plans for Rolling Stock
Check availability of resources
Maintenance calendar
Consolidated picture for the planning unit
Check against safety thresholds
Predicted Life and Health Indicators
Detailed Information on Infrastructure
• “Real” Big Data in action: hundreds of TB, huge number of sources and entities involved
• Complex algorithms to predict indicators and optimize the outcome across multiple dimensions
• Huge transformational value and financial impact
© 2015 SAP SE or an SAP affiliate company. All rights reserved. 16 Customer
Life and Health Indicators for Trains Doors
Number of Cycles
Time of Cycle
Now Last maintenance operation
1
2
3
1 Past values are monitored by sensors on board of trains
Projection of life indicator considering the plans for the material, prediction of the consequence on the health indicator using mathematical methods
Health safety threshold – function of life
Trigger of maintenance request due to the life indicator reaching risk threshold
2
3 4
4
Maintenance scheduled according
to life
© 2015 SAP SE or an SAP affiliate company. All rights reserved. 17 Customer
Life and Health Indicators for Trains Doors
Now Last maintenance operation
1
2
Past values are monitored by sensors on board of trains
Projection of life indicator considering the plans for the material, prediction of the consequence on the health indicator using mathematical methods
Health safety threshold – function of life
Trigger of maintenance request due to predicted health issues
4
3
Maintenance scheduled according
to life
Number of Cycles
Time of Cycle 1
2
3
4
© 2015 SAP SE or an SAP affiliate company. All rights reserved. 18 Customer
Life Indicator / Time Relationship – Example L1
Time
Life
Now Maintenance scheduled according
to time
Mai
nte
nan
ce
sch
edu
led
acc
ord
ing
to li
fe
1
2
3 1 Line describing the expected aging of the component based on a linear relationship with time (and KM), which is the basis of current maintenance scheduling
Curve describing the effective evolution and projection of the synthetic function of life indicators
Time for which the maintenance can be postponed based on life indicators, compared to time / km based planning
2
3
Last maintenance operation
© 2015 SAP SE or an SAP affiliate company. All rights reserved. 19 Customer
Life Indicator / Time Relationship – Example L2
Time
Life
Now Maintenance scheduled according
to time
Mai
nte
nan
ce
sch
edu
led
acc
ord
ing
to li
fe
1 Line describing the expected aging of the component based on a linear relationship with time (and KM), which is the basis of current maintenance scheduling
Curve describing the effective evolution and projection of the synthetic function of life indicators
Area of risk of failure, because of the component likely wearing out faster than foreseen in the standard maintenance plan
2
3
1
2
3
Last maintenance operation
© 2015 SAP SE or an SAP affiliate company. All rights reserved. 20 Customer
Life Indicators in Action: Braking System
Objective and Approach
Calculation of the life indicator energy dissipation by friction braking systems, with separated analysis for locomotives and coaches
Development and test of calculation algorithms for all the possible cases identified
Pressione in Condotta Generale e Cilindro Freno
Energia dissipata cumulata per le carrozze
Results Achieved
Monitoring of the effective usage and level of wear-out for every single component of the braking system against the risk thresholds identified
© 2015 SAP SE or an SAP affiliate company. All rights reserved. 21 Customer
Life Indicators in Action: Braking System
• High variability of the energy dissipated
per km clearly indicates that distance is
not a good indicator of consumption for
braking systems
• Comparison between traditional
indicators such as km and more precise
life indicators highlights the significant
opportunity for optimization of
maintenance operations
© 2015 SAP SE or an SAP affiliate company. All rights reserved. 22 Customer
Life Indicators in Action: Braking System
• High variability of the energy dissipated
per km clearly indicates that distance is
not a good indicator of consumption for
braking systems
• Comparison between traditional
indicators such as km and more precise
life indicators highlights the significant
opportunity for optimization of
maintenance operations
© 2015 SAP SE or an SAP affiliate company. All rights reserved. 23 Customer
Anomaly Detection Analysis on Turnouts
• 19 turnouts analyzed over a period of around 17
months
• Diagnostic equipment records tension and current of
the actions
• Over the period there were only 11 actual failures,
corresponding to an MTBF (Mean Time Between
Failures) of around 21,000 hours
• Turnouts are inspected on the average once a
month
Data seem to indicate a situation of over-maintenance,
driven by the strong priority of avoiding failures almost
at any cost. This is typical of critical assets where
failures come with big penalties
© 2015 SAP SE or an SAP affiliate company. All rights reserved. 24 Customer
Analysis on Turnout 1
1
2
3
1
2
1
2
3
1 1
0
0,5
1
1,5
2
2,5
3
3,5
20/0
8/2
01
42
1/0
8/2
01
42
2/0
8/2
01
42
3/0
8/2
01
42
4/0
8/2
01
42
5/0
8/2
01
42
6/0
8/2
01
42
7/0
8/2
01
42
8/0
8/2
01
42
9/0
8/2
01
4 ...
05/0
9/2
01
40
6/0
9/2
01
40
7/0
9/2
01
40
8/0
9/2
01
40
9/0
9/2
01
41
0/0
9/2
01
41
1/0
9/2
01
41
2/0
9/2
01
41
3/0
9/2
01
41
4/0
9/2
01
41
5/0
9/2
01
4 ...
06/1
2/2
01
40
7/1
2/2
01
40
8/1
2/2
01
40
9/1
2/2
01
41
0/1
2/2
01
41
1/1
2/2
01
41
2/1
2/2
01
41
3/1
2/2
01
41
4/1
2/2
01
41
5/1
2/2
01
4 ...
01/0
2/2
01
50
2/0
2/2
01
50
3/0
2/2
01
50
4/0
2/2
01
50
5/0
2/2
01
50
6/0
2/2
01
50
7/0
2/2
01
50
8/0
2/2
01
50
9/0
2/2
01
51
0/0
2/2
01
51
1/0
2/2
01
51
2/0
2/2
01
51
3/0
2/2
01
51
4/0
2/2
01
51
5/0
2/2
01
5 ...
01/0
4/2
01
50
2/0
4/2
01
50
3/0
4/2
01
50
4/0
4/2
01
50
5/0
4/2
01
50
6/0
4/2
01
50
7/0
4/2
01
50
8/0
4/2
01
50
9/0
4/2
01
51
0/0
4/2
01
51
1/0
4/2
01
51
2/0
4/2
01
51
3/0
4/2
01
51
4/0
4/2
01
51
5/0
4/2
01
5 ...
01/0
7/2
01
50
2/0
7/2
01
50
3/0
7/2
01
50
4/0
7/2
01
50
5/0
7/2
01
50
6/0
7/2
01
50
7/0
7/2
01
50
8/0
7/2
01
50
9/0
7/2
01
51
0/0
7/2
01
51
1/0
7/2
01
51
2/0
7/2
01
51
3/0
7/2
01
51
4/0
7/2
01
51
5/0
7/2
01
5
Anom
aly
score
in t
he 2
0-d
ay
period
DATA
Failure
Thresholds on anomalies defined as a combination of severity and number of instances in a rolling period, and
tuned to match violations and prevalence of failures, at least in terms of order of magnitude
© 2015 SAP SE or an SAP affiliate company. All rights reserved. 25 Customer
Analysis on Turnout 2
1 1 1 1 1
2
3
1 1
2
3
1
2
1 1 1
2 2
1 1
0
0,5
1
1,5
2
2,5
3
3,5
15/0
1/2
01
4
17/0
1/2
01
4
19/0
1/2
01
4 ...
09/0
3/2
01
4
11/0
3/2
01
4
13/0
3/2
01
4
15/0
4/2
01
4
17/0
4/2
01
4
19/0
4/2
01
4 ...
16/0
7/2
01
4
18/0
7/2
01
4
20/0
7/2
01
4
25/0
8/2
01
4
27/0
8/2
01
4
29/0
8/2
01
4 ...
10/0
9/2
01
4
12/0
9/2
01
4
14/0
9/2
01
4
20/1
0/2
01
4
22/1
0/2
01
4
24/1
0/2
01
4 ...
07/1
2/2
01
4
09/1
2/2
01
4
11/1
2/2
01
4
13/1
2/2
01
4
03/0
2/2
01
5
05/0
2/2
01
5
07/0
2/2
01
5
09/0
2/2
01
5
11/0
2/2
01
5
13/0
2/2
01
5
15/0
2/2
01
5
05/0
4/2
01
5
07/0
4/2
01
5
09/0
4/2
01
5 ...
06/0
5/2
01
5
08/0
5/2
01
5
10/0
5/2
01
5
05/0
6/2
01
5
07/0
6/2
01
5
09/0
6/2
01
5 ...
23/0
6/2
01
5
25/0
6/2
01
5
27/0
6/2
01
5
07/0
7/2
01
5
09/0
7/2
01
5
11/0
7/2
01
5 ...
08/0
8/2
01
5
10/0
8/2
01
5
12/0
8/2
01
5
01/0
9/2
01
5
03/0
9/2
01
5
05/0
9/2
01
5
Anom
aly
score
in t
he 2
0-d
ay
period
DATA
Failure
© 2015 SAP SE or an SAP affiliate company. All rights reserved. 26 Customer
Analysis on Turnout 3
2 2
5
9 9 9 9 9 9 9 9
10
3
0
2
4
6
8
10
12
Anom
aly
score
in t
he 2
0-d
ay
period
DATA
Failure
© 2015 SAP SE or an SAP affiliate company. All rights reserved. 27 Customer
Results of Anomaly Detection Analysis on Turnouts
Data are analyzed in time series, but presented discretized by week
• 5 out of the 11 failures were predicted by the
algorithm a few days in advance
• 6 failures were not predicted, best hypothesis is
that around half could be identified with better
diagnostic equipment, half is highly intractable
from a data science perspective
• 69 false positives were also generated
Very high accuracy and specificity
Very low precision
Prevalence (number of weeks in which a failure
happens / total number of weeks analyzed) is
below 1%
In rare event prediction case, achieving high
accuracy, precision and sensitivity at the same time
is extremely challenging
The real actionable insight often sits in the
Negative Predictive Value
© 2015 SAP SE or an SAP affiliate company. All rights reserved. 28 Customer
Anomaly Detection on Electric Engines of Locomotives
Failures
© 2015 SAP SE or an SAP affiliate company. All rights reserved. 29 Customer
Anomaly Detection on Electric Engines of Locomotives
Failure
© 2015 SAP SE or an SAP affiliate company. All rights reserved. 30 Customer
Anomaly Detection on Electric Engines of Locomotives
Failure
Conclusions
© 2015 SAP SE or an SAP affiliate company. All rights reserved. 32 Customer
Transforming the Game in Maintenance Operations
Risk
Cost Definition of maintenance policies can be seen as the management of the tension and trade-off between costs and risks. Most of the efficiency efforts are aimed at making sure that the right balance is reached
Internet of Things-based maintenance innovation represents the opportunity of transforming structurally the system and reach a new level of balance between costs and risks
© 2016 SAP SE or an SAP affiliate company. All rights reserved. 33 Customer
Remember all the sofisticated models….
@ 2016 SAP SE or an SAP affiliate company. All rights reserved. Customer 8
Principal Component Analysis of Switches
Anomaly Detection with Principal Component Analysis scores
Weibull Remaining Useful Life Estimation
Battery behavioural groupings
Wasserstein Metric for battery performance analysis
K-Mediod cluster analysis to partition the population into classes of similar devices
PCA
Automatic Rule Extraction with Decision Trees
Expert Rule Validation
© 2016 SAP SE or an SAP affiliate company. All rights reserved. 34 Customer
….need to work under the conditions of the “real world” eventually!
@ 2016 SAP SE or an SAP affiliate company. All rights reserved. Customer 8
Principal Component Analysis of Switches
Anomaly Detection with Principal Component Analysis scores
Weibull Remaining Useful Life Estimation
Battery behavioural groupings
Wasserstein Metric for battery performance analysis
K-Mediod cluster analysis to partition the population into classes of similar devices
PCA
Automatic Rule Extraction with Decision Trees
Expert Rule Validation
© 2015 SAP SE or an SAP affiliate company. All rights reserved. 35 Customer
Discovery Workshop
Data Modeling
Validation Iterations
O P T I O N A L P R O O F O F C O N C E P T
G O - L I V E
I M P L E M E N T A T I O N
( S A P S E R V I C E S / C D )
< 3 months
PdMS Go-Live
CDP (optional)
Extending to Meet Business Needs Optional PoC is an opportunity to increase customer value proposition
Implementation
• Understand
and reframe the
problem(s)
• Ideate possible
solutions
• Ensure
technical
feasibility
• Determine
standard out-
of-the-box fit
• Evaluate
technical
feasibility
• Weekly iterations
to collect feedback
• Pass acceptance
tests and/or
success criteria
• Prepare data
• Model data
• Generate
derived signal
models
• Standard
Implementation
• Based on SAP
standard PdMS
product
• Custom
development of
new functionality
• Go-Live Support
• Standard Support
• SAP CD Support
(optional)
• Application
Management
Service (AMS)
(optional)
S T A R T
© 2014 SAP SE or an SAP affiliate company. All rights reserved.
Thank you
Contact information:
Thomas Klyvø
+45 2923 3573