Predictive Maintenance by Sending PI Notifications to SAP PM to Initiate Automatic Maintenance Tasks
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1 Predictive Maintenance by Sending PI Notifications to SAP PM to Initiate Automatic Maintenance Tasks Presented by Jean-Pierre Vande Maele EMEA USERS CONFERENCE 2015 Copyright 2015 OSIsoft, LLC
2 Predictive Maintenance by sending PI Notifications to SAP PM to initiate automatic maintenance tasks Jean-Pierre Vande Maele 2
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 Jean-Pierre Vande Maele 3
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 : employees Sales EUR 10.2 billion Jean-Pierre Vande Maele 4
5 Divisions and products Consumer Board Packaging Solutions Biomaterials Wood Products Paper Jean-Pierre Vande Maele 5
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: tonnes/year Jean-Pierre Vande Maele 6
7 Production News-Line, PM4 Machine width: 10.4 m Maximum speed: m/min ~120 km/h Product: standard newsprint paper, gsm Nominal production capacity: t/y Raw material: 100% PfR Jean-Pierre Vande Maele 7
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 Jean-Pierre Vande Maele 8
9 HOW IS THE PI SYSTEM USED AT STORA ENSO LANGERBRUGGE? Jean-Pierre Vande Maele 9
10 PI LAYOUT tags 100 users since 2001 IBA Viconsys PI Clients MIS Plant Application SQL Server Reporting Services SAP-PM MES Optivison Matrikon OPC Server MicroSoft BizTalk file file s PI ACE PI AF Analyses Service PI Data Archive PI AF PI Notifications PI OPC Interfaces Distributed Control System/PLC Provox DeltaV Metso DNA Modico n Siemen ss7 ABB MicroScad a ABB QCS ABB Drives ABB MNS Jean-Pierre Vande Maele 10
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 Jean-Pierre Vande Maele 11
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 Jean-Pierre Vande Maele 12
13 WHY PREDICTIVE MAINTENANCE? Jean-Pierre Vande Maele 13
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 Jean-Pierre Vande Maele 14
15 HOW IS PREDICTIVE MAINTENANCE IMPLEMENTED? Jean-Pierre Vande Maele 15
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, Jean-Pierre Vande Maele 16
17 Business Case : Follow-up of Drives (type : Vacon) Jean-Pierre Vande Maele 17
18 PREDICTIVE MAINTENANCE PI SAP-PM Interface Jean-Pierre Vande Maele 18
19 BENEFITS OF PREDICTIVE MAINTENANCE Jean-Pierre Vande Maele 19
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 Results in less unplanned downtimes Jean-Pierre Vande Maele 20
21 KEY SUCCESS FACTORS Jean-Pierre Vande Maele 21
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 Jean-Pierre Vande Maele 22
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) Jean-Pierre Vande Maele 23
24 PILOT PROJECT STARTED IN STORA ENSO LANGERBRUGGE Alarm Audit [Company]
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. Jean-Pierre Vande Maele 25
26 TRENDMINER context Live monitoring Add context to events Searching on multiple dimensions Indexing historian for faster search Jean-Pierre Vande Maele 26
27 TRENDMINER architecture? Web-Client Virtual Machine No impact on existing infrastructure* Historian data connector TrendMiner Virtual Machine Jean-Pierre Vande Maele 27
28 TRENDMINER how? Add search dimensions & filtering Add operational context Order results See historical results Jean-Pierre Vande Maele 28
29 TRENDMINER fingerprinting & monitoring Anomaly warning! Fingerprint of 2 tags over multiple results Jean-Pierre Vande Maele 29
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 Jean-Pierre Vande Maele 30
31 Jean-Pierre Vande Maele 31
32 Questions Please wait for the microphone before asking your questions Please don t forget to Complete the Online Survey for this session State your name & company EMEA USERS CONFERENCE 2015 Copyright 2015 OSIsoft, LLC 32
33 Thank You EMEA USERS CONFERENCE 2015 Copyright 2015 OSIsoft, LLC
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