maintenance optimization - prose• the trend is to move towards predictive maintenance • planning...

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

9

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

40

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