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Maintenance OptimizationLukas Bach, SINTEF - Optimization
Agenda
2
Current practice1
Optimization opportunities2
Predictive maintenance3
New optimization challenges4
Conclusions5
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
• Generic solver for real-life vehicle routing
• World records for scientific test-bench instances
Optimizing transportation logistics
• 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
• 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
• Scheduling sports leagues and tournaments
• Using mathematical programming
• Professional
• Non-professional
Sports scheduling
7
Agenda
8
Current practice1
Optimization opportunities2
Predictive maintenance3
New optimization challenges4
Conclusions5
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
… … … … …
Maintenance objective
10Maintenance task Task deadline Lost utilization time
a) Estimated deadlines
b) Ideal execution
c) Early execution
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maintenance activity
0
10
20
30
40
50
60
1 2 3 4 5 6 7 8 9 10 11 12
Man
hou
rs
Week
4 weeks 4 weeks
Solution (infeasible)maintenance activityrolling stock
0
10
20
30
40
50
60
1 2 3 4 5 6 7 8 9 10 11 12
Man
hou
rs
Week12
Solution (infeasible)maintenance activityrolling stock
0
10
20
30
40
50
60
1 2 3 4 5 6 7 8 9 10 11 12
Man
hou
rs
Week13
0
10
20
30
40
50
60
1 2 3 4 5 6 7 8 9 10 11 12
Man
hou
rs
Week
Solution (infeasible)maintenance activityrolling stock
Preponed 1 x
14
0
10
20
30
40
50
60
1 2 3 4 5 6 7 8 9 10 11 12
Man
hou
rs
Week
Solution (infeasible)maintenance activityrolling stock
15
0
10
20
30
40
50
60
1 2 3 4 5 6 7 8 9 10 11 12
Man
hou
rs
Week
Solutionmaintenance activityrolling stock
Preponed 1 x
16
Alternative solution (infeasible)
0
10
20
30
40
50
60
1 2 3 4 5 6 7 8 9 10 11 12
Man
hou
rs
Week
maintenance activityrolling stock
17
0
10
20
30
40
50
60
1 2 3 4 5 6 7 8 9 10 11 12
Man
hou
rs
Week
Alternative solutionmaintenance activityrolling stock
Preponed 1 x
18
0
10
20
30
40
50
60
1 2 3 4 5 6 7 8 9 10 11 12
Man
hou
rs
Week
Solution optimizedmaintenance activityrolling stock
19
Agenda
20
Current practice1
Optimization opportunities2
Predictive maintenance3
New optimization challenges4
Conclusions5
0
200
400
600
800
1000
1200
1400
Jan Feb Mar Apr Mai Jun Jul Aug Sep Okt Nov Des
Man
hou
rs
Month
Optimization Permanent man hours: 1330
Optimization Permanent man hours: 1076
0
200
400
600
800
1000
1200
1400
Jan Feb Mar Apr Mai Jun Jul Aug Sep Okt Nov Des
Man
hou
rs
Month
Optimization
0
200
400
600
800
1000
1200
1400
Jan Feb Mar Apr Mai Jun Jul Aug Sep Okt Nov Des
Man
hou
rs
Month
Permanent Temporary
Permanent man hours: 1057
Optimization
0
200
400
600
800
1000
1200
1400
Jan Feb Mar Apr Mai Jun Jul Aug Sep Okt Nov Des
Man
hou
rs
Month
Original peak
Agenda
25
Current practice1
Optimization opportunities2
Predictive maintenance3
New optimization challenges4
Conclusions5
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
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
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
Typical decision support
Component ARolling stock # 1 2 3 4 5 6 7Risk of failure
31
Component BRolling stock # 1 2 3 4 5 6 7Risk of failure
Agenda
32
Current practice1
Optimization opportunities2
Predictive maintenance3
New optimization challenges4
Conclusions5
0
200
400
600
800
1000
1200
1400
Jan Feb Mar Apr Mai Jun Jul Aug Sep Okt Nov Des
Man
hou
rs
Month
Optimization
0
200
400
600
800
1000
1200
1400
Jan Feb Mar Apr Mai Jun Jul Aug Sep Okt Nov Des
Man
hou
rs
Month
Optimization Permanent man hours: 1330
Optimization Permanent man hours: 1076
0
200
400
600
800
1000
1200
1400
Jan Feb Mar Apr Mai Jun Jul Aug Sep Okt Nov Des
Man
hou
rs
Month
0
500
1000
1500
2000
2500
Jan Feb Mar Apr Mai Jun Jul Aug Sep Okt Nov Des
Man
hou
rs
Month
Standard 100 % 75 % 25 % Previous peak
Optimization
0
500
1000
1500
2000
2500
Jan Feb Mar Apr Mai Jun Jul Aug Sep Okt Nov Des
Man
hou
rs
Month
Standard 100 % 75 % 25% Previous peak Optimized
Optimization
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
Agenda
39
Current practice1
Optimization opportunities2
Predictive maintenance3
New optimization challenges4
Conclusions5
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
