| Aircraft type : | Business jet |
|---|---|
| Mean flights duration : | 2 hours |
| Mean flights cycles per day : | 0.5 |
| AOG costs : | 500$ per hour |
| Aircraft type: | Single Aisle |
|---|---|
| Flights mean duration: | 1.5 hours |
| Flights mean quantity per day: | 6 |
| AOG costs: | 10 000$ per hour |
| Part | Yearly cost per aircraft | Your score |
|---|---|---|
|
Oil filter i
Test
|
$ | |
|
Shock absorber i
Test
|
$ | |
|
Anti-skid valve i
Test
|
$ | |
|
Hydraulic pump i
Test
|
$ |
| Function: | To control brakes |
|---|---|
| Possible causes of failure: | Leakage |
| Mean Time Between Failure: | 7000 flight cycles |
| In case of failure: | NOGO |
| Mean time to repair | |
|---|---|
| hours | |
| Workman cost | |
| $ | |
| Spare part + logistic cost | |
| $ | |
| Aircraft on ground | |
| Yes |
| Mean time to repair |
|---|
| 2 hours |
| Workman cost |
| 800$ |
| Spare part & logistic cost |
| 5 500$ |
| Aircraft on ground |
| No |
|
Periodicity
|
| Every flight cycles |
| Mean time to repair |
|---|
| 2 hours |
| Workman cost |
| 800$ |
| Spare part + logistic cost |
| 5 500$ |
| Aircraft on ground |
| No |
| Invest in predictive system If yes yearly cost is : 1 000 $ per AC |
|---|
|
|
| Objective |
| Predict a failure 10 flights (minimum) in advance |
| Parameter to monitor |
|
T = Fluid temperature (C°) D = Cumulated duration at high temperature (minutes) |
| Algorithm |
|
Threshold on Cumulated duration at high temperature (T-
Tlimit)*D > Dlimit Limit-Curve n° |
| Function: | To filter contaminants from engine oil |
|---|---|
| Possible causes of failure: | Filter clogging |
| Mean Time Between Failure: | 200 flight hours |
| In case of failure: | NOGO |
| Mean time to repair | |
|---|---|
| hours | |
| Workman cost | |
| $ | |
| Spare part + logistic cost | |
| $ | |
| Aircraft on ground | |
| Yes |
| Mean time to repair |
|---|
| 1 hour |
| Workman cost |
| 200$ |
| Spare part + logistic cost |
| 210$ |
| Aircraft on ground |
| No |
|
Periodicity
|
| Every flight hours |
| Mean time to repair |
|---|
| 1 hour |
| Workman cost |
| 200$ |
| Spare part + logistic cost |
| 210$ |
| Aircraft on ground |
| No |
| Invest in predictive system if yes, cost is 500$ per AC |
|---|
|
|
| Objective |
| Predict a failure 10 flights (minimum) in advance |
| Parameter to monitor |
| DeltaP : delta Pressure (normalized) |
| Algorithm |
| Function: | To maintain appropriate hydraulic pressure |
|---|---|
| Possible causes of failure: |
|
| Mean Time Between Failure: | 20 000 flight hours |
| In case of failure: | NOGO |
| Mean time to repair | |
|---|---|
| hours | |
| Workman cost | |
| $ | |
| Spare part + logistic cost | |
| $ | |
| Aircraft on ground | |
| Yes |
| Mean time to repair |
|---|
| 2 hours |
| Workman cost |
| 800$ |
| Spare part + logistic cost |
| 8 500$ |
| Aircraft on ground |
| No |
|
Periodicity
|
| Every flight hours |
| Mean time to repair |
|---|
| 2 hours |
| Workman cost |
| 800$ |
| Spare part + logistic cost |
| 8 500$ |
| Aircraft on ground |
| No |
| Invest in predictive system if yes, yearly cost is 1 000$ per AC |
|---|
|
|
| Objective |
| Predict a failure 10 flights (minimum) in advance |
| Parameter to monitor |
|
Critical health state indicator calculated by an algorithm
based on : Flow rate, pressure and temperature measurements Hybridation with a physical model of the pump AI classification trained on failure manifestations |
| Algorithm |
| Function: | Reduce the impact force at landing |
|---|---|
| Possible causes of failure: |
|
| Mean Time Between Failure: | 10 000 flight cycles |
| In case of failure : | NOGO |
| Mean time to repair | |
|---|---|
| hours | |
| Workman cost | |
| $ | |
| Spare part + logistic cost | |
| $ | |
| Aircraft on ground | |
| Yes |
| Mean time to repair |
|---|
| 6 hours |
| Workman cost |
| 4 800$ |
| Spare part + logistic cost |
| 5 500$ |
| Aircraft on ground |
| No |
|
Periodicity
|
| Every flight cycles |
| Mean time to repair |
|---|
| 6 hours |
| Workman cost |
| 4 800$ |
| Spare part + logistic cost |
| 5 500$ |
| Aircraft on ground |
| No |
| Invest in predictive system if yes, yearly cost is 1 000$ per AC |
|---|
|
|
| Objective |
| Predict a failure 10 flights (minimum) in advance |
| Parameter to monitor |
|
Vz : Vertical velocity (at touchdown) - normalized Az : Vertical acceleration (at touchdown) - normalized |
| Algorithm |
$
| Score > 80 | Excellent ! |
| 80 > Score > 50 | Good ! |
| 50 > Score > 25 | Better optimize your predictive maintenance ! |
| 25 > Score | You should implement predictive maintenance ! |
| Part | Yearly cost per aircraft | Your score |
|---|---|---|
| Oil filter | $ | |
| Shock absorber | $ | |
| Anti-skid valve | $ | |
| Hydraulic pump | $ |
secs
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