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5.3 Off-line Inference by Experts
Diagnosing the Beam Propeller Movement
of the Frontal Stand
Stand and Potmeter/Encoder and LUC _Extension (feedback). So, these components are
possibly at false if POSITION_ERROR is true.
It is important to notice that, these three clarifying predictions of system behavior can only be made
by means of such a concise representation of the system as Figure 5.3. Without it you would have
to trust the expert’s inference of system behavior. It is true that some experts know this information
by head, and they use it to diagnose the fault scenarios. However, many times the information is not
as comprehensive as Figure 5.3 shows. Moreover, an expert is unaware of using some information.
Therefore, this knowledge is obtained by many interviews and by studying documentation [17, 16,
19].
The fault categories used in Table 5.4 can be explained by the introduced knowledge. Fault
categories C1 and C2, describe resp. an unhealthy control loop and an unhealthy speed loop. The
fault category C3 describes an occurrence of a POSITION_ERROR that seems to reveal an unhealthy
position loop. However, the values of the Pact and Pset variables do not differ more than the
threshold, and therefore the LUC_Extension falsely generates the POSITION_ERROR. It is possible
that the error is caused by a malfunctioning PEU. From now on, the fact whether or not Pact and
Pset differ more than the threshold is denoted by the variable e_pos. When e_pos is 1 the threshold
is violated, otherwise e_pos is 0. Fault scenario C4 is an occurrence of a POSVAL_ERROR. This
indicates that the PEU is broken. The symptoms described above can be described more formally as
follows:
CURRENT_ERROR = 1 ∨
SPEED_ERROR = 1 ∨
POSITION_ERROR = 1 ∧ e_pos = 1 ∨
POSITION_ERROR = 1 ∨
POSVAL_ERROR = 1 (5.2)
The variables that are used in the specification of the symptoms are the observables on the system:
CURRENT_ERROR, SPEED_ERROR, POSITION_ERROR, POSVAL_ERROR, and e_pos. The identification
of the symptoms enables the construction of the table that defines the mapping of these symptoms
on diagnoses. This table is presented in the next subsection.
5.3.3 Results of Off-line Inference by Experts
The LUT is constructed by manually performed deductive and abductive reasoning of all possible
values of the observables. Table 5.5 shows the result. For each possible observation, the table
defines a mapping on a set of consistent diagnoses. For presentation purposes, the table shows
only single faults. The table could be implemented by some expert system. In such an automated
fault diagnosis approach, the monitored system data is searched in the table, inserted into the expert
system, and the expert system produces a list of possible diagnoses. However, in the approach of
this case study, the constructed LUT is just an intermediate step; Table 5.5 is used for constructing
a consistency-based model.
The entropy of the approach to fault diagnosis ’off-line inference’ can be calculated, by using
the complete (including multiple faults) LUT. This yields the following result for the entropy (H)
that experts are able to achieve:
H expert = 0.9453 (5.3)
The next section presents a MBD implementation, that achieves the same entropy.
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