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Chapter 5
Diagnosing the Beam Propeller
Movement
of the Frontal Stand
The previous chapter presented the basic theory of model-based fault diagnosis. It already included
an explanation of MBD on a real subsystem of the Philips Cardio-Vascular X-Ray System. This
trivial example already shows benefits, but the main part of the Philips Cardio-Vascular X-Ray
System has more complexity. Therefore, this chapter presents a case study of model-based diagnosis
on a much more complex subsystem.
In this case study, a MBD implementation for diagnosing failures of the beam propeller movement
is developed. The LYDIA tool set is used as a diagnostic engine, and the LYDIA language is
used for specifying the model. The first section of this chapter describes the experimental methodology
and the approach to the case study. Section 5.2 describes the scope of the target system.
The third section presents the fault diagnosis that experts are able to do using log data. Then, the
three remaining sections deal with the model-based approach to fault diagnosis. Each of the sections
presents a MBD implementation, and estimates its diagnostic performance in terms of entropy. Section
5.4 presents a MBD implementation (MBD-1) that achieves equal entropy as experts are able to
achieve. Section 5.5 presents a MBD implementation (MBD-2), that uses an improved model on the
same system data, and calculates its entropy gain. The final section of this chapter presents which
potential MBD implementation (MBD-3) could achieve the highest entropy gain, by considering
various extra measurement points.
5.1 Experimental Methodology
Recall that the problem encountered in the work presented in this thesis is to improve fault diagnosis
of Philips Cardio-Vascular X-Ray Systems with respect to higher dependability. This case study
presents a proof-of-concept of the model-based approach to fault diagnosis, aimed at Philips Cardio-
Vascular X-Ray Systems. The goal of this case study is to study the applicability of MBD on
an example subsystem of the Philips Cardio-Vascular X-Ray System, namely the beam propeller
movement of the frontal stand. The best way to do this is by using a metric for accuracy. Accuracy
is generally considered as the most important criterion for diagnostic performance.
MBD produces a list of diagnoses, in order of probability. If a supervisory user would like to
use the result of MBD for its recovery actions, only one of the solutions can be accepted. The first
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