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4.3 Basic Assumptions Model-Based Fault Diagnosis
probability calculation of the LYDIA tool uses this assumption. Despite this, it is possible to reason
about dependent faults. The inference mechanism of the MBD technique specifies a dependency
between faults, the inference mechanism takes this into account.
Other topics that are not yet fully solved by MBD technology are state and time.
The previous part of this chapter briefly introduced the basics of MBD and the LYDIA approach
to MBD. The remainder of this chapter introduces some more advanced topics. It is restricted to
the parts that are necessary to achieve the goal of the project described in this thesis: presenting a
proof-of-concept of a model-based approach to fault diagnosis, aimed at the Philips Cardio-Vascular
X-Ray System. This proof-of-concept, that will be presented in the next chapter, requires that MBD
achieves a higher diagnostic performance than the current approach. A higher diagnostic performance
can be achieved by:
1. Improving the diagnostic engine. Better and more sophisticated algorithms for solving
a propositional model could increase the speed of diagnosis. This way, also larger-sized
diagnostic problems become feasible. The work of [15] and [14] is in this area.
2. Improving the model. If the model defines more relevant information, the diagnostic resolution
and accuracy of diagnoses increase.
3. Increasing the observability. A system that is in its use phase has a fixed observability.
Part of the observations can be observed automatically, because it is available in some digital
format. Other observations can only be made manually. Extra sensors could increase the
observability of the system, and improve accuracy of diagnoses and diagnostic resolution.
Considering the three points mentioned above, what are the important topics of model-based diagnosis,
in respect to the work presented in this thesis?
• ad 1. Currently, the time to produce a diagnosis is taking days, while a successful modelbased
approach is able to produce diagnosis within milliseconds. Consequently, the proofof-concept
does not require improvement of the diagnostic engine. A very slow algorithm
already improves speed of diagnosis.
• ad 2. Improving a model, on the other hand, is within the scope of this thesis. The next
chapter discusses a case study of the model-based approach, and better models in this work
allow for a proof of higher diagnostic performance. Section 4.4 introduces types of models,
and ways to take on the modeling problem. In order to optimize the solution to the problem,
Section 4.5 introduces a metric that can be used to estimate the quality of a model.
• ad 3. Increasing observability is likely to increase diagnostic performance. Although, the
Philips Cardio-Vascular System is fixed in respect to this work, a promising advantage is
the following: the metric introduced in Section 4.5 allows to study which additional measurements
have the highest effect on the accuracy. This way, it is possible to improve the
diagnostic performance of systems in the design phase.
So, improving the diagnostic engine is not important for this work, increasing observability is nice,
but modeling is very important. Therefore, the next section introduces the modeling activity.
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