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Automated Fault Diagnosis
3.1 Overview Techniques
1. The first quadrant depicted in Figure 3.1 shows approaches that use a consistency-based
model for deriving diagnoses. A manual method includes a human that knows the consistencybased
model by head, and applies deductive reasoning for each failure that occurred, to come
up with a diagnosis. This is one of the ways service engineers at PMS, and expert at PMS,
diagnose systems. In an automated approach the consistency-based model is formalized,
and the deductive inference of diagnoses is automated by a solver. This technique is called
Model-Based Diagnosis, and is introduced in the following chapter. If the consistency-based
model only specifies structural information, it is not possible to deductively derive anything.
In this case, it is possible to use induction when pass/failed-outcomes of output observables
are available. This method is explained in Section 3.2.
2. The second quadrant depicted in Figure 3.1 shows approaches that use an abductive model. A
manual method includes a human that has abductive knowledge about the system, combines
this with real-life observations, and applies abductive reasoning to come up with diagnoses
(this is also done by service engineers and experts at PMS). In an automated method the
inference is done off-line in order to construct a LUT. One might say that service engineers,
or an automated entity, that is given a LUT only use black box information. This is true, but
these approaches are still categorized as white box because the information for constructing
this LUT is white box information. Furthermore, it is important to notice is that the LUT
can also be constructed using a consistency-based model, as delimited by the arrow between
verb—deduction— and LUT.
3. The third quadrant depicted in Figure 3.1 shows approaches that only use real-life observations
of input and output values, and a history of diagnoses (black box information combined
with diagnostic experience). For example, by diagnostic experience a service engineer knows
that a high value of some counter indicates that a certain power supply might be broken.
This results in observation-diagnosis tuples, that can be used as examples to the inductive
reasoning process. This way symptom-on-diagnosis mappings are derived, and a LUT is constructed.
Service engineers at PMS that have much experience with a system use inductive
reasoning. This way, they implicitly constructed a LUT by head. An automated implementation
of inductive reasoning using black box information is a data mining technique. This
method is described in Section 3.2. Like in the second quadrant, the LUT specifies effect-tocause
reasoning, and using it to diagnose the system can be implemented in a manual way, or
automated way.
4. The fourth quadrant is not applicable (N/A). It is not possible to reason from cause-to-effect
using only black box information.
Table 3.1 shows the approaches that are considered in this thesis. PMS-1, PMS-2, PMS-3 and
PMS-4 are four approaches that are used at Philips Medical Systems. PMS-1 refers to the activity
within PMS in which Symptom-Cause-and-Solution sheets, Error-on-Solution database and FIPs
are constructed. These are the LUTs constructed and used at PMS. The project in which Philips
Cardio-Vascular X-Ray Systems are remotely monitored (ServiceWAX) provides another LUT, and
is also referred to by PMS-1. The difference with the other LUTs is that the expert uses real-life
observations in its inference, instead of using observations made in test situations or observations
that he/she would expect. PMS-2 refers to the way experts, and very skilled service engineers,
diagnose systems when faced with a failure. PMS-3 refers to the way of reasoning in which service
engineers learn symptoms-on-diagnosis mappings from experience. PMS-4 refers to the way service
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