SAP Predictive Maintenance and Service & SAP Asset ... Leonardo Digital... · Defined in SAP PM OEM...

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


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


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


Enabling collaborative asset management

Combined together

to deliver

SAP Asset

Intelligence

Network


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


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


Enable OEM and Operator collaboration using a Digital Twin


Model

Digital Twin

Physical Assets

Defined in SAP PM

OEM 1

Operator

20 MVA 3Phase

Transformer

20 MVA 3Phase

Transformer - 120 MVA 3Phase



Transformer - n

20 MVA 3Phase


Transformer - 2

20 MVA 3Phase


Transformer - n

OEM 2

OEM 3


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


Building the network


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


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



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



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


SAP Asset Intelligence NetworkContent - AttributesSAP Asset Intelligence Network

Content - Attributes


SAP Asset Intelligence NetworkContent – Structure and Spare PartsSAP Asset Intelligence Network

Content – Structure and Spare Parts


SAP Asset Intelligence NetworkContent – Integrated 3D VisualizationSAP Asset Intelligence Network

Content – Spare Parts using 3D Visualization


SAP Asset Intelligence NetworkContent – 3D Work InstructionsSAP Asset Intelligence Network

Content – Work Instructions


SAP Asset Intelligence NetworkContent – DocumentsSAP Asset Intelligence Network

Content – Documents


SAP Asset Intelligence NetworkApplication – AnnouncementSAP Asset Intelligence Network

Application – Announcement


SAP Asset Intelligence NetworkApplication – Measuring PointsSAP Asset Intelligence Network

Application – Measuring Points


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


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.

https://launchpad.support.sap.com/#/notes/2382303


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


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)


Agenda


• AIN Overview


• Integration

• Business Cases

PdMS Overview


• PdMS Overview






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


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


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


Company

Owns and operates a

fleet of around

2,000 electro-trains,

2,000 locomotives

and 30,000 coaches

and wagons

Customer ExampleTrain Operator


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*

*


Solution

Customer ExampleTrain 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

Run to Failure Preventive On-Condition Predictive


Agenda


• AIN Overview


• Integration

• Business Cases

PdMS Overview


• PdMS Overview






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


SAP Predictive Maintenance and ServiceSolution components and value drivers


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


SAP Predictive Maintenance and ServiceSystem and component level visualizations




Asset Health Control Center

Asset Health

Control Center

Asset Health

Fact Sheet





Logistics & Maintenance

Execution Systems


Asset Health Fact Sheet


SAP Predictive Maintenance and ServiceInsight Provider Catalog

*”Health Status Overview” is an example of a custom Insight Provider built using SDK


SAP Predictive Maintenance and ServiceAsset Health Control Center

Asset View

New Orleans Refinery

Houston Refinery

Asset View




Asset View


Houston Refinery

Asset View

Asset Hierarchy

Asset Hierarchy




Asset View


Houston Refinery

Asset View

Asset Hierarchy

Asset Hierarchy Insight Provider Catalog





Asset View Asset Hierarchy








Insight Providers

Insight Provider Catalog Insight Providers






Insight Providers



Insight Providers

Remaining

Useful Life

2

5

12

18

22

32

Remaining

Useful Life

2

10

5

8

13

16





Insight Providers



Insight Providers





Insight Providers



Insight Providers





Insight Providers



Insight Providers





Insight Providers



Insight Providers

*”Health Status Overview” is an example of a custom Insight Provider built using SDK





Insight Providers



Insight Providers


SAP Predictive Maintenance and ServiceAsset Health Fact Sheet

Equipment View

Equipment View

Asset Heath Control Center

Asset Health

Control Center


Serial #12345

Remain

ing

Useful

Life 2

5

1

2

1

8

2

2

3

2



Equipment View

Equipment View

Asset Heath Control Center

Asset Health

Control Center


Serial #12345

Remain

ing

Useful

Life 2

5

1

2

1

8

2

2

3

2





Equipment View Asset Health

Control Center







Control Center



Insight Provider Catalog Insight

Providers

Insight Providers




Control Center




Providers

Insight Providers




Control Center




Providers

Insight Providers




Control Center




Providers

Insight Providers


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


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


Agenda


• AIN Overview


• Integration

• Business Cases

PdMS Overview


• PdMS Overview






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


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




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


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


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.


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


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


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*

Date post:	10-Feb-2019
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