Aerospace and Electronic Systems Magazine March 2017 - 10
Refining Fault Trees Using Aviation Definitions for Consequence Severity
Table 7.
CSL Promotion Table
CSL
(+)
A=0
A=1
A=2
A=3
A=4
M=0
Ω0
Ω1
Ω2
Ω3
Ω4
M=1
Ω1
Ω2
Ω3
Ω4
Ω4
M=2
Ω2
Ω3
Ω4
Ω4
Ω4
M=3
Ω3
Ω4
Ω4
Ω4
Ω4
M=4
Ω4
Ω4
Ω4
Ω4
Ω4
Table 8.
CSL Mitigation Table
CSL
(−)
A=0
A=1
A=2
A=3
A=4
M=0
Ω0
Ω1
Ω2
Ω3
Ω4
M=1
Ω0
Ω0
Ω1
Ω2
Ω3
M=2
Ω0
Ω0
Ω0
Ω1
Ω2
M=3
Ω0
Ω0
Ω0
Ω0
Ω1
M=4
Ω0
Ω0
Ω0
Ω0
Ω0
Table 9.
CSL Lower Threshold Table
CSL
(MAX)
A=0
A=1
A=2
A=3
A=4
M=0
Ω0
Ω1
Ω2
Ω3
Ω4
M=1
Ω1
Ω1
Ω2
Ω3
Ω4
M=2
Ω2
Ω2
Ω2
Ω3
Ω4
M=3
Ω3
Ω3
Ω3
Ω3
Ω4
M=4
Ω4
Ω4
Ω4
Ω4
Ω4
Table 10.
CSL Upper Threshold Table
10
CSL
(MIN)
A=0
A=1
A=2
A=3
A=4
M=0
Ω0
Ω0
Ω0
Ω0
Ω0
M=1
Ω0
Ω1
Ω1
Ω1
Ω1
M=2
Ω0
Ω1
Ω2
Ω2
Ω2
M=3
Ω0
Ω1
Ω2
Ω3
Ω3
M=4
Ω0
Ω1
Ω2
Ω3
Ω4
In some sense, the four gates that operate on modifiers can be
considered as generalizations of the operators used in conventional
and fuzzy logic. Of the CSL gates defined, the upper threshold gate
(Table 10) is the closest analogy to the AND operator (Table 6). In
fact, the two are essentially identical.
The advantages of CSL over conventional binary and fuzzy
logic are twofold. First, by replacing conventional fault trees
with CSL networks (and individual faults with fault chains), it
is now possible to map all fault events directly to a consequence,
a necessary step to allow consistent analysis with more than one
severity level. Second, by introducing a wider range of modifiers,
it is easier to model events that increase or decrease the severity
of a fault.
An implementation detail is that some modifiers promoting
(increasing) severity are actually instances in which a particular
fault event occurs more than once. Such promotion modifiers
are perhaps best modeled by repeatedly combining a fault state
with itself using an OR gate. An example is provided in the
appendix.
USE CASE
In order to evaluate the utility of CSL analysis, we consider a use
case. Specifically, we consider a UAS inspection application in
which the aircraft flies a grid pattern, as shown in Figure 4. Currently, in the United States, such applications are limited by the
restriction of having a human pilot within line of sight of the vehicle [1]. Eventually, however, it is hoped that countless numbers
of UAS will operate autonomously, with no remote pilot required.
As a first step toward enabling autonomy, a small-scale FMEA and
an accompanying CSL analysis for collision risk were conducted.
The FMEA considered a hybrid implementation in which the UAS
mission would be conducted through both remote piloted and autonomous operations.
Table 11 provides a list of faults and associated consequences
for the autonomous inspection (F01 to F11). The table also lists
modifiers including mitigations (M01 to M04) and promotions
(P01 to P05). The descriptions of these events are compatible
with either remote piloted or autonomous flight. For instance, a
loss of sensor that causes the UAS to collide with another flying object (e.g. another UAS or a bird) might indicate the loss
of visual contact by a remote pilot (due to an occlusion, a distraction, glare from the sun, etc.) or a loss of a sensor used for
autonomous flight (such as a sense-and-avoid radar). Though the
fault descriptions apply both to remote pilot and autonomous operations, the actual probability distribution values vary for each
context.
It would seem that the risk of damage during a collision with
terrain is different for a UAS than for a conventional aircraft.
For example, crashing into an open field would likely be just
a minor event if the UAS were unscathed, or major if the UAS
were severely damaged. On the other hand, crashing into a playground densely populated with children could be catastrophic
[10]. In the inspection application illustrated above, the consequences would likely be in the hazardous category were the
inspection UAS to crash into the building (and not catastrophic
IEEE A&E SYSTEMS MAGAZINE
MARCH 2017
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