Reliability Maintenance Engineering Day 2 session 2 Reliability Techniques
day live course focused on reliability engineering for maintenance programs. Introductory material and discussion ranging from basic tools and techniques for data analysis to considerations when building or improving a program.
11. Corrective Action
• Plan Do Check Act
process
• Experiments and
measures
• Verify results
• Document results
Plan
Do
Check
Act
12. An Example
• Define problem
• Plan response
• Do experiment
• Monitor results
• Implement fix
13. Monitor Effectiveness
• What do you measure?
• What can you measure?
• Does absence of failure
mean it’s fixed?
– What is expected failure
rate?
– What is probability of
failure?
14. Monitoring
• How to determine
monitoring plan?
• Sample size?
• Duration?
• Focus on failure
mechanism
17. Repair rate over time
• Trend plots
– MCF – cumulative
failures over time
– Inter arrival times
– Reciprocals of inter
arrival times
• Duane Plot
• Sample Data
• 1500 hour test
• With each failure RCA
and improvements
implemented
Observed failure times
5, 40, 43, 175, 389, 712,
747, 795, 1299, 1478 hrs
18. Cumulative failure vs. time (MCF)
• Failure count vs time
• Straight line (roughly)
means steady failure
rate.
• Curve down –
improvement over time
• Curve up – increasing
problems over time
NIST Engineering Statistics Handbook 8.2.2.3
19. Inter arrival failure times
• Plot waiting time
between failures
• Trend up –
improvement
• Trend down –
degradation
• Straight – no change
over time
20. Reciprocal Inter arrival times
• Plot failure rate
estimates since last
failure
• Trend up –
• degradation
• Trend down –
improvement
• Straight – no change
over time
24. Predictions & Forecasts
Risks
• Might just be wrong
• May miss failure
mechanisms due to
masking
• Unable to predict
everything
Benefits
• Awareness of
probability of failure
• Awareness of what to
expect to fail
• Maintenance and
logistics planning
25. Predictions & Forecasts
• Empirical models
– Extension of field data
– Extensions of
experiments
– Extensions of vendor
data
• First principle models
– Difficult to create
– Does it apply in this
situation
26. Cautions
• Check assumptions
• Check sensitivity
• Verify models and fits
• Be conservative
The further from reality
the more risk of being
wrong exists
27. Monitor and Adjust
• All models are wrong,
some are useful
• Continue to refine and
challenge any model
• RCA with focus on
failure mechanisms is
key to success
29. Summary
• Examining opportunities and
reliability improvement
techniques for robust design
– RCM, FMEA, RCA, 6 step and
FRETT
• Developing corrective actions
and determining
effectiveness
• Implementing the growth
curve technique
• Fault and failure forecasting
Reliability techniques
to improve performance
Notas do Editor
FRETT - There are literally hundreds of examples of a properly structured root-cause failure analysis and upgrade program yielding immediate and measurable payback. Fortunately, such programs are deceptively simple and quickly implemented by anyone who wishes to do so. We call it the FRETT approach because it recognizes that, without exception, the basic agents of machinery component and part failure mechanisms are always force, reactive environment, time or temperature1. These basic failure mechanism agents may combine to hasten component degradation.http://www.plantservices.com/articles/2006/239.html6 steps - http://pdf.usaid.gov/pdf_docs/pnach088.pdf
Corrective action and effectiveness
Heinz P. Bloch, P.E., is owner of Process Machinery Consulting (www.heinzbloch.com) in Westminster, Colorado, and the author of numerous articles and books, including “Improving Machinery Reliability” and “Pump Wisdom.” Contact him at heinzpbloch@gmail.com.
Balance between investment and value
Examining opportunities and reliability improvement techniques for robust designRCM, FMEA, RCA, 6 step and FRETT
Balance between investment and value
Reliability growth
A straight line means the NHPP applies – the slope is the rate of improvementThe reliability improvement slope for virtually all reliability improvement tests will be between 0.3 and 0.6. The lower end (0.3) describes a minimally effective test - perhaps the cross-functional team is inexperienced or the system has many failure mechanisms that are not well understood. The higher end (0.6) approaches the empirical state of the art for reliability improvement activities.