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Chapter 6
Conclusions and Recommendations
The complexity crisis is most visible in complex, multi-disciplinary embedded systems. Although
individual components might work well separately, the system as a whole may exhibit unexpected
faults. More and more components are bought third party and might not be used as the supplier
expected when developing them. There is a complex dynamic interaction between subsystems, and
this is what makes fault diagnosis difficult and time consuming. The Philips Cardio-Vascular X-
Ray system is a complex, multi-disciplinary embedded system. The current efforts for doing fault
diagnosis at PMS is increasing; the portion of the PMS employees working on service instead of
development grows. This thesis presents possible improvement when using a model-based approach
to fault diagnosis.
6.1 Conclusions
In this thesis, it is shown that the current practice to fault diagnosis at PMS is not optimal with
respect to the items that an ideal fault diagnosis technique would have; accuracy, speed of diagnosis,
uncertainty of diagnosis, context independency, development costs, runtime costs, explanation
facility, and adaptability. Furthermore, model-based diagnosis is chosen as the automated solution
that could improve fault diagnosis, because this technique to automated fault diagnosis is likely to
possess these items that, when present in fault diagnosis, improve dependability of the target system.
Unfortunately, it is not possible to conclude that MBD improves fault diagnosis with respect to
higher dependability of Philips Cardio-Vascular X-Ray Systems. In order to so, it must be possible
to determine the accuracy of both the current practice to fault diagnosis, and the proposed technique
MBD. This cannot be done. The reason for this is that it is impossible to find out what faults caused a
certain failure. Although, in many cases, it is possible to determine the observations at the moment
of failure (the error detection mechanisms in the Philips Cardio-Vascular X-Ray System produce
clear log entries), it is impossible to determine the actual fault that caused the specific observable
values. Without this information, the accuracy of the current practice, and a MBD implementation,
cannot be quantified. In other words, the significant problem is the inability to determine what
components recovered a certain failure.
Entropy is a valuable metric to show the improvement of different MBD implementations. In the
case study, the diagnostic performance of three MBD implementations that can diagnose the beam
propeller of the frontal stand is estimated. The entropy gain of implementation MBD-2 compared to
MBD-1 is zero, thus implementing MBD-2 is not useful. Adding extra sensors to the target system
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