maintenance optimization - prose• the trend is to move towards predictive maintenance • planning...
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Maintenance OptimizationLukas Bach, SINTEF - Optimization
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Agenda
2
Current practice1
Optimization opportunities2
Predictive maintenance3
New optimization challenges4
Conclusions5
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Close relationships generate innovation and high quality research
3
BUSINESSProduct development and the
application of research results
THE UNIVERSITIESBasic research and
education
SINTEFMultidisciplinary applied
contract research
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• Generic solver for real-life vehicle routing
• World records for scientific test-bench instances
Optimizing transportation logistics
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• Real time arrival and departure sequencing / scheduling
• Surface routing combined with runway management
• Improved efficiency (punctuality increased by 60%) with a more manageable workload (less airplanes moving at the same time)
Optimizing air traffic
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• Dynamic and multi-modal journey planner (public transport, car sharing, public bikes,…)
• Routing of flexible (on-demand) buses for first-last/mile transportation
• Laying the groundwork for autonomous vehicles
Mobility as a service
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• Scheduling sports leagues and tournaments
• Using mathematical programming
• Professional
• Non-professional
Sports scheduling
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Agenda
8
Current practice1
Optimization opportunities2
Predictive maintenance3
New optimization challenges4
Conclusions5
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Maintenance program
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Activity Execution cycle Subtask Type of labor # man hours
Passenger doors inspection after 3 months
electric circuits specialized 2
mechanical pieces manual 0.5
Brakes inspection after 10K km
pads manual 2
cylinder specialized 0.5
valves specialized 1
… … … … …
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Maintenance objective
10Maintenance task Task deadline Lost utilization time
a) Estimated deadlines
b) Ideal execution
c) Early execution
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maintenance activity
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4 weeks 4 weeks
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Solution (infeasible)maintenance activityrolling stock
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Solution (infeasible)maintenance activityrolling stock
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Solution (infeasible)maintenance activityrolling stock
Preponed 1 x
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Solution (infeasible)maintenance activityrolling stock
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Solutionmaintenance activityrolling stock
Preponed 1 x
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Alternative solution (infeasible)
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maintenance activityrolling stock
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Alternative solutionmaintenance activityrolling stock
Preponed 1 x
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Solution optimizedmaintenance activityrolling stock
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Agenda
20
Current practice1
Optimization opportunities2
Predictive maintenance3
New optimization challenges4
Conclusions5
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Optimization Permanent man hours: 1330
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Optimization Permanent man hours: 1076
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Jan Feb Mar Apr Mai Jun Jul Aug Sep Okt Nov Des
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Optimization
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Permanent Temporary
Permanent man hours: 1057
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Optimization
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Original peak
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Agenda
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Current practice1
Optimization opportunities2
Predictive maintenance3
New optimization challenges4
Conclusions5
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Predictive maintenance - Goals and Challenges
• More robust rail operations• Reduce risk of break downs during operation
• Reduced maintenance• Less frequent maintenance
• Planning and scheduling challenges• "Normal" preventive maintenance is recurring• Predictive maintenance fluctuates• Proper planning becomes more important
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Predictive maintenance
• What can we predict?• Or at least detect!
• Non-predictable• Possible to measure failure• Impossible / too expensive to detect failure
• Predictable• Data collection• Methods
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How do we do it?
• Accurate data is essential!• More data is not necessarily the solution
• What do we do with the data?
• AI:• Statistics• Machine learning
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Monitor and sensor data
Failure prediction
Optimal maintenance planning
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Types of failure
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0 10 20 30 40
Risk
of f
ailu
re
Weeks0 10 20 30 40
Risk
of f
ailu
re
Weeks
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Types of failure
0 2 4 6 8
Risk
of f
ailu
re
Weeks
30
0 10 20 30 40
Risk
of f
ailu
re
Weeks
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Typical decision support
Component ARolling stock # 1 2 3 4 5 6 7Risk of failure
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Component BRolling stock # 1 2 3 4 5 6 7Risk of failure
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Agenda
32
Current practice1
Optimization opportunities2
Predictive maintenance3
New optimization challenges4
Conclusions5
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Optimization
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Optimization Permanent man hours: 1330
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Optimization Permanent man hours: 1076
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Jan Feb Mar Apr Mai Jun Jul Aug Sep Okt Nov Des
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0
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Standard 100 % 75 % 25 % Previous peak
Optimization
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Standard 100 % 75 % 25% Previous peak Optimized
Optimization
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Planning example
Probability of failure in week:
Task Hours Cost 1 2 3 4 5
1 3 5
2 5 3
3 3 4
4 6 1
5 6 6
6 1 8
7 4 2 Due in week 3
8 2 2 Due in week 4
9 6 6 Due in week 538
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Agenda
39
Current practice1
Optimization opportunities2
Predictive maintenance3
New optimization challenges4
Conclusions5
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Conclusions
• The trend is to move towards predictive maintenance
• Planning becomes more complex, optimization is necessary to achieve:• Less maintenance
• Reduced maintenance cost
• More robust train operations• Less breakdowns during operation
• Potentially less total maintenance
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Teknologi for et bedre samfunn