pdf download - Software and Computer Technology - TU Delft

5.5 MBD Implementation 2
Diagnosing the Beam Propeller Movement
of the Frontal Stand
5.4.5 Results of the MBD-1 Implementation
The goal of this step was to define a MBD implementation to fault diagnosis that achieves the same
entropy as experts are able to achieve. The LUT, that specifies the outcome of the LYDIA diagnostic
engine for all possible observations, is exactly equal to Table 5.5. Consequently, the entropy (H) of
the MBD implementation (MBD-1) is the same:
H MBD−1 = 0.9453 (5.4)
5.5 MBD Implementation 2
This sections presents a second iteration of steps 2, 3, and 4 of the case study approach. The
intended result is that the uncertainty decreases without having to make additional measurements,
but by improving the model. The idea is as follows. A good diagnostic approach takes advantage
of every possible inference that could be drawn out of all available system data. Therefore, in this
iteration, experts and modeler search for more symptoms within the examined system data.
If one tries to ’discover’ more symptoms in the same system data, it is useful to discretize the
system data. Most data of the fault scenarios shown in Table 5.4 is in the domain of integers. Humans
experience difficulties to reason about faulty components using such a level of detail. As
explained in the previous chapter, humans are better able to perform fault diagnosis using a domain
with few members. The applied discretization is defined by source domain N and target domain M.
The target domain M is defined as follows:
⎧
T L,
⎪⎨
M =
⎪⎩
: Too Low. The threshold has been violated.
L, : Low. Negative polarity of the value.
N, : Nominal. Close to zero.
H, : High. Positive polarity of the value.
T H : Too High. The threshold has been violated.
The source domain is the domain of natural numbers N. The target domain values can be chosen
in many ways. In this case these particular values (L denotes negative polarity, N denotes close
to zero, etc.) are meaningful when reasoning from first principles about the target system. The
mapping of values in the source domain N to the target domain M is shown in Appendix B.5. Table
5.6 shows the discretized values of the observations. Note that the table shows other variables than
shown in Table 5.4. Discretizing the variables Pact and Pset is not useful, because the system does
not show different behavior for different angles of the frontal stand. The variables Imin and Imax
are used to calculate the polarity of Iset and Iact (see Appendix B.5), and do not have value on
their own.
The values of Table 5.6 are much easier to interpret than the raw system data of Table 5.4.
Much of the discretized variables have values that experts expect (see Section 5.3). A current error
implies that the Iact and Iset differ. A speed error implies that Vact and Vset differ. From system
behavior, it is also expected that the value of Iset has the same polarity as the value of e_sp. This
does not hold for fault scenario 3, as can be seen in Table 5.6. In this fault scenario, the inequality
of Iset and e_sp is a symptom of a malfunctioning system. Figure 5.3 shows that CTR_speed must
be at false. This unit is of lower than the chosen granularity, and therefore the symptom identifies
that the LUC_Extension is malfunctioning.
62