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2.4 Optimal Fault Diagnosis
State-of-the-Practice
Fault diagnosis at PMS
• false alarm: If a component is diagnosed to be faulty while it is healthy, costs increase.
At least, if you assume that the diagnosis is being used to bring the system in a better
state 3 .
• missed diagnosis: If a component is diagnosed healthy while it is not, a failure is likely
to reoccur and degrade the dependability.
2. Speed of diagnosis. The sooner an error and its fault is identified, the less impact a failure
has. A fast diagnostic process increases the availability of the system. In other words, the
customer downtime will be minimized.
3. Low Uncertainty. Also known as diagnostic resolution or isolability. This is the extent to
which a diagnostic process is able to minimize the set of suspicious components. It can be
measured by using entropy. The more a root cause of failure is isolated, the less repair time
is needed. Consequently, costs decrease and the availability of the system increases.
4. Context independency. If the successful working of the diagnostic process highly depends on
forces outside the sphere of influence of the company, obviously risks increase (e.g., dependance
on employees, network of others, etc.).
5. Low development costs. Development costs are all the costs that have to made prior to the
start of the diagnostic process. These are the costs for developing all the diagnostic tools (e.g.,
supporting artifacts, training sessions for troubleshooters, other prepare actions that precede
the operational phase).
6. Low runtime costs. Runtime time costs are all the costs that the company makes to keep the
diagnostic process up and running.
7. Explanation facility. The justification of a diagnosis helps in evaluating diagnostic decisions.
It increases the trust in a dependable operation of the system. Also, the quality of the system
can be evaluated and improved.
8. Adaptability. Design changes occur frequently. There is always need for more/other functionality
and better internal working. Therefore, the ability of a diagnostic process to cope
with design changes is a prime concern. A failing in this ability results in a decrease of all
other attributes that are listed in this list.
The following items would add to perfectness, but are outside the scope of this thesis:
• Robustness. This refers to the extent to which a diagnostic process can handle unexpected
situations.
• Novelty Identifiability. The ability to detect and diagnose faults that did not occur before.
• Ability to deal with multiple faults.
• Reasonable storage and computational requirement.
Although these 4 items are important attributes in an ideal approach to fault diagnosis, the work
that this thesis presents does not discuss these items. However, the technique presented in Chapter
4 could deal with these items, but in the case study of 5 the diagnostic performance in respect to
robustness, novel identifiability, multiple faults, and storage and computation requirement is not
examined.
3 At PMS this means that a component that is diagnosed as unhealthy will be replaced.
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