Predictive Maintenance by Sending PI Notifications to SAP PM to Initiate Automatic Maintenance Tasks

(1)

EMEA USERS CONFERENCE 2015

Presented by

Predictive Maintenance by

Sending PI Notifications to SAP

PM to Initiate Automatic

Maintenance Tasks

(2)

2

Predictive Maintenance by sending

PI Notifications to SAP PM to initiate

automatic maintenance tasks

(3)

AGENDA

• Introduction Stora Enso Langerbrugge

• How is the PI System used at Stora Enso Langerbrugge?

• Why Predictive Maintenance?

• How is Predictive Maintenance implemented?

• Benefits of Predictive Maintenance

• Key Success Factors for implementing Predictive Maintenance

• Pilot Project “Trend Mining”

• Questions & Answers

(4)

4

Stora Enso is the global rethinker of the paper, biomaterials, wood

products and packaging industry. We always rethink the old and

expand to the new to offer our customers innovative solutions based on

renewable materials.

Key figures 2014 :

27 000 employees

Sales EUR 10.2 billion

(5)

Divisions and products

Packaging Solutions

Biomaterials Wood Products

Consumer Board Paper

(6)

6

Stora Enso Langerbrugge

• Founded in 1932

• Producer of newsprint and magazine paper • Situated in the harbour of Ghent

– More than 80 million inhabitants in a radius of 300 km •  380 employees: 30% white collars and 70% blue collars • Production capacity: 555.000 tonnes/year

(7)

7

Production

News-Line, PM4

• Machine width: 10.4 m • Maximum speed: 2.000 m/min ~120 km/h • Product: standard newsprint paper, 40-52 gsm • Nominal production capacity: 400.000 t/y • Raw material: 100% PfR

(8)

8

Production

Two biomass fueled CHP’s

• 55 and 125 MWth output • Incineration:

– internal sludge of de-inking and water treatment

– external biomass • Energy production two

CHP’s:

– 100% need for steam – >70% need for

electricity

• As of 2016: start supply of green heat in de harbor of Ghent

(9)

HOW IS THE PI SYSTEM USED

AT STORA ENSO LANGERBRUGGE ?

9 Jean-Pierre Vande Maele

(10)

PI LAYOUT – 140.000 tags – 100 users – since 2001

Provox DeltaV Metso DNA

ABB QCS

Distributed Control System/PLC

Modico n Siemen sS7 ABB MicroScad a ABB Drives ABB MNS PI OPC Interfaces MIS Plant Application Matrikon OPC Server PI ACE file s file s PI Clients PI AF Analyses Service PI AF PI Notifications PI Data Archive MES Optivison Viconsys IBA MicroSoft BizTalk SAP-PM SQL Server Reporting Services

(11)

PI SYSTEM USE IN LANGERBRUGGE

• Very easy

to work with & easily interface to all different suppliers (OPC

connection)

• DIFFERENT Departments

:

Production, Engineering, Energy, Quality, Purchase,

Supply Chain & Management

• Business GOVERNANCE Model

• DIFFERENT Targets

– Daily maintenance & Monitoring

– Troubleshooting

– KPI & Support for daily Production meetings

(12)

PI SYSTEM USE FOR MAINTENANCE

• Daily

check by users

• Use of

Excel and PI DataLink

to follow-up the assets

• Automatic background analyses

:

• Temperature evolution, motor loads, ..

• Alarms

(using Excel conditional formatting) are based on

one point in

time

and a static alarm threshold is used

• Manual notifications

in SAP PM resulting in workorders

Based on more than 10 years of experience

(13)

WHY PREDICTIVE MAINTENANCE?

(14)

WHY PREDICTIVE MAINTENANCE?

• Automatic control

24 hours a day – 7 days a week

– Daily check of the Excel files, not certain that this will happen due to variations in work-load etc. – Daily check required in order to capture failures, during the weekend 2 days are

lost…

• Eliminating Alarms based on one point in time

used in order to become more

accurate

• Automatic notification

in SAP PM

• BECOME MORE EFFICIENT

– Problem detection and solving – Our Business processes eliminating

human interventions as much as possible

(15)

HOW IS PREDICTIVE MAINTENANCE

IMPLEMENTED?

(16)

HOW IS PREDICTIVE MAINTENANCE IMPLEMENTED?

• Use of Asset Framework (AF) and Notifications

• SAP PM Assets

are copied and updated in AF

• Based on

AF Element template for a specific type of asset

- standardization

- combine static data (from external table) with dynamic data and calculations

• Dynamic Alarm thresholds

are based on a mathematic model that resembles the

actual process characteristic – the temperature-current relation is a first order (linear)

characteristic

• Automatic notifications

in SAP PM

• In case of a notification, all relevant data is

available

in one place

for a senior

maintenance engineer:

- location (room, cabinet), voltage, …

(17)

Business Case : Follow-up of Drives (type : Vacon)

(18)

Jean-Pierre Vande Maele 18

PREDICTIVE MAINTENANCE

PI – SAP-PM Interface

(19)

BENEFITS OF PREDICTIVE MAINTENANCE

(20)

BENEFITS

PREDICTIVE MAINTENANCE

• Automatic

check 24/7

• More accuracy

due to dynamic alarm threshold settings

• With PI Notifications

only problems

are reported, this saves time:

no need to go through various Excel files

• Automatic notifications in SAP

assuring the latest issues are discussed in the

daily production meetings and resulting in work orders

(21)

KEY SUCCESS FACTORS

(22)

KEY SUCCESS FACTORS

• Identify pilot project – Begin small

• Change Management

• Requires time – learning curve

• Identify Business Sponsor

• Involve motivated key user(s)

• Show quickly first success

• Data analysis experience in organization is required

• Close collaboration between IT and Automation (maintenance)

• Use intelligent Middleware interface PI Server – SAP PM

(23)

FUTURE PLANS

• Predictive Maintenance: Extend to other asset types

• Train more staff to use AF and Notifications

• Develop additional process monitoring tools

For example: use AF/Notifications to follow-up chemical

dosing – to avoid overdosing (health issues, …) or under

dosing (quality loss)

(24)

Alarm Audit [Company]

PILOT PROJECT STARTED IN

STORA ENSO LANGERBRUGGE

(25)

WHY TREND MINING SOLUTION?

• BIG DATA challenge !

• Make historian data searchable

• Modeling analysis is labour intensive & not flexible

• Historian server lacks content

• Retroactive search does not help for proactive warning.

(26)

TRENDMINER – context

• Indexing historian for faster search

• Searching on multiple dimensions

• Add context to events

(27)

TRENDMINER – architecture?

• No impact on existing infrastructure*

• Historian data connector

• TrendMiner Virtual Machine

Virtual Machine

Web-Client

(28)

TRENDMINER – how?

• Order results

• See historical results

• Add operational context • Add search dimensions & filtering

(29)

TRENDMINER – fingerprinting & monitoring

• Anomaly warning!

• Fingerprint of 2 tags over multiple results

(30)

POTENTIAL BENEFITS

• Reduce data analysis time by process engineers

• Resolution time of unplanned downtimes

• Knowledge retention

• Early event detection for process / asset related issues to

reduce number of unplanned downtimes

(31)

(32)

EMEA USERS CONFERENCE 2015

32 Questions

Please wait for the

microphone

before asking your questions

Please don’t forget to…

Complete the Online Survey

for this session

http://eventmobi.com/emeauc15

State your

(33)