1. David Baglee Erkki Jantunen
Critical Study of Essential Elements linked with the adoption of Condition-Based Maintenance
David Baglee
Erkki Jantunen
University of Sunderland UK
VTT Finland

2. Presentation Outline Aim & overview of research Key findings
Next stages
Whats new on the horizon for maintenance management?

3. Where are we?

4. Who are we? Medium sized University 17.000 students 1500 staff
Four Faculties
Campus in London
Strong reputation in Advanced Manufacturing
Strong links with industry

5. Background to Research
UK government report in 2013 suggested Auto-supply chain in UK suffer from high levels of equipment failure
Lack of evidence to support
General statement (broad brush)
Size of supply chain?
12% increase a year in numbers produced from 2009
Small scale research project at Sunderland (Nissan suppliers)

6. Background to Research cont’d
“Quick and dirty” results showed OEE 54%
25 suppliers in different Tier.
Discussions with different industries
Discussions with colleagues in Europe
Spain
Italy
Finland

7. Pan-European Approach
From a range of industries including auto, aerospace, process, marine and paper, collected & analysed data on different maintenance practices (SME)
Discussions
Scattered studies
Expert opinions
literature
Do you need to move to an ‘improved’ maintenance strategy i.e. CBM HOW?

8. CBM
Look
Listen
Smell
taste

9. Benefits of CBM
early detection of impending machinery failure to help lower the risk of unscheduled downtime
scheduling and utilisation of maintenance resources is more efficient
reduced likelihood of ‘maintenance induced’ failures
etc etc

10. Implement CBM
Important to use an appropriate method for modelling deterioration, the different conditions and their effects, and the optimal selection and scheduling of inspections and preventive maintenance actions.
Failure characteristics often grouped into a number of categories

11. Bathtub Curves

12. Which to monitor? Infant mortality, useful life, rapid wear out
Rapid wear out after long useful life
Gradual wear out after long useful life

13. Results Industry Country Infant mortality, useful life, rapid wear out
Rapid wear out after long useful life
Gradual wear out after long useful life
Aerospace UK
10%
70%
Chemical industry
Finland
Process industry
France
30%
Mechanical components
Spain
50%
Auto
21%
22%
60%
15%
Rail 5%
Russia
20%
Mining industry
Canada

14. Review of Data Expected and ‘unexpected’ differences
Majority state “gradual wear out after long useful life”
Process industry UK / Sweden
High infant mortality, useful life, rapid wear out
Rail industry Spain:
Rapid wear out after long useful life
Process Industry France:
30% all three

15. Time to reflect

16. Meaning ‘behind’ the data
Wide range of variations in results
Lack of ‘credible’ data?
Often unknown, why, how, when
Lack of Historical data
Improper use of equipment
Bathtub curve used as a simple method

17. Conclusion Data from study is ‘generally’ unreliable
Although - expected differences and similarities do exist
Big difference between countries in same sector
Need a new approach to data collection and analyses
CBM could be ‘appropriate’ if the above are addressed

18. Companies Response
Not this bad - Blame was passed around

19. What’s next? Start again- To achieve-
Interview again with a different set of metrics
Widen the range of companies and staff
Different experts from more companies
To achieve-
Accurate and up to date data
Historical trends
To understand the how, why and when
To help develop a new approach to maintenance

20. A new approach? What is new in maintenance?
: Basic and routine maintenance, reactive maintenance, only repair when broke
: Planned preventive maintenance, Time based approach. Total Productive Maintenance (TPM), team based approach to maintenance, examines “culture and people” not systems
1980-Present: Condition monitoring (CM), hazard studies, failure modes and effect analysis. Reliability centred maintenance (RCM) Computer aided maintenance management. Multi-skilled workforce. Emphasis on equipment reliability and availability
Present - ?: Self Maintenance systems...

21. What is the next ‘Big Bang’ maintenance strategy
Artificial Intelligence (thinking / smart machines)
Augmented Reality
New technologies
Data collection and analyses
Add cost factor to OEE
Awareness programmes
??????

22. Smart system?

23. Smart systems

24. Sensors monitoring health Identify and report problems
Smart systems
Sensors monitoring health
Identify and report problems
Root cause
Future Health
Plan production etc

25. Smart systems

26. Augmented Reality?

28. Conclusion Nothing new in maintenance since 1980s?
Do we need a new approach to maintenance using technology i.e. AR
More needed on the ‘importance of maintenance’
Research into new technologies to support existing systems

29. Thank you for your attention