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Conclusions and Recommendations
6.2 Recommendations
• In the case study, it is assumed that faults occur independently of each other. In real-case
fault scenarios, it is common that fault are dependent. In other words, one fault is caused by
other faults. Identifying the root cause of dependent faults increases a dependable operation
of the target system. Therefore, it is valuable to examine MBD implementations for such fault
scenarios.
• Practice at PMS shows that the number of faults and failures is highest just after a new system
release. A MBD implementation that identifies faults in the implementation, that did not
occur until that point scores high on the item ’novel identifiability’. A MBD model is able to
model fault modes that did not occur yet, but is is essential that the model is still robust. Consequently,
a research on novel identifiability and the robustness of MBD models is valueble
for PMS, and industry in general.
• Currently, for many cases, the time to diagnose is long because it is not known if the cause of
a failure is caused by hardware or software. Therefore, it is value to examine the benefits of
MBD in such a case.
• The storage and computational requirements to calculate the entropy of models, and entropy
gain of additional measurements, is very high for most models. Therefore, it is useful to
examine implementations of the diagnostic engine that approximate entropy in less time and
space.
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