Information Technology Reference
In-Depth Information
that indicates if this particular risk is estimated as
very important
or not [9],
the unavailability of HBD (
I
0
−HBD
) and the number of hours of operation
(
h
DBC
).
4
Evaluation of the model and implementation on a
high-speed line
In this section, each previous sub-function is evaluated thanks to a proba-
bilistic approach. Then, this model is applied for a high speed line case.
4.1
Evaluation of sub-functions and activities
To perceive anomalies on crossed trains The estimated rate of non-de-
tection of a hot box (
RDH
) by the train driver of a cruiser train (
TCD
)is
represented by equation 1.
RDH
TDA
VIQ
RDH
TCD
=
(1)
×
TCQ
- Assuming an average train driver attention for his driving environment:
RDH
TDA
=
10
−
2
/h.
- Assuming that only 50% of cases detected by the driver of a train cruiser
are visual and 90 % of the information perceived visually in the process
of human perception is taken into account [10].
VIQ
=
5
.
= 0,45.
- Assuming that the driver spends 10% of his time to monitor the train he
meets:
TCQ
= 0,1 (10 %).
0
,
9
×
0
,
5
The result of
RDH
TCD
gives a number greater than 1. It is proposed to
neglect the effect of this activity in the model.
To detect problems on their own train The estimated rate of non-
detection of a hot axle box by the train driver is represented by equation
2.
RDH
TDA
WSQ
RDH
Driver
=
(2)
×
TCQ
- Assuming an average train driver attention to his driving environment:
RDH
TDA
=
10
−
2
/h.
- The only supervised wagons of the trains are the locomotive or 20% of
train:
WSQ
= 1 or 0,2.
- Assuming that the driver spends 25% of his time to monitor the state of
its own train:
TCQ
= 0,25.
5
.
10
−
1
/
h and for the rest of the train:
RDH
Driver
=1/h.Itisagain proposed to
neglect the effect of this activity in the model.
Results for this activity are: for the locomotive:
RDH
Driver
=
2
.
