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6.2 Recommendations Conclusions and Recommendations
(MBD-2) does lead to entropy gain, and might be worth the costs of placing these sensors. Entropy
can be used to decide which additional measurements are best.
6.2 Recommendations
The following recommendations help in establishing a fault diagnosis approach at PMS, that achieves
higher dependability of Philips Cardio-Vascular Systems. Appendix D shows the ideal practice of a
fault diagnosis process at PMS.
• Currently, it is impossible to determine what fault(s) caused a certain failure. This must be
solved, in order to evaluate the diagnostic performance of the current practice, and enable
comparison with new approaches to fault diagnosis, such as MBD. The observations when a
failure occurred are known and stored in the logs of each system, and by means of remote
monitoring in a central database. The problem could be solved when Service Engineers insert
their diagnosis of a failure, and what it did to recover the failure, into the central database.
A tool must use the time of this insertion, and knowledge about which log entries contain
observations about the failure, to link the diagnosis to the observations. For example, suppose
a service engineers diagnoses a failure of the beam propeller movement of the frontal stand,
namely fault scenario S13 (see Table 5.13). The database contains an ’error 11’ log entry. In
the proposed situation, the service engineer records the failure ’beam propeller movement of
the frontal stand’, the diagnosis that MBU is at fault, the replacement of the MBU, and the time of
these actions. If the failure does not reoccur within short time, the automated tool concludes
that the observables of the error 11 entry, with a time stamp close to the entry inserted by the
service engineer, recovers the failure. Consequently, the central database must save the link
between both the ’error 11’ entry and the entry inserted by the service engineer. This enables
PMS to know what fault caused the failure.
• Entropy is an important metric when developing MBD implementations. Its use as a feedback
mechanism for constructing an optimal model, and optimal measurement points is very valuable.
Research on how to achieve the highest entropy gain in fault diagnosis of the Philips
Cardio-Vascular X-Ray Systems is valuable for research as well as business. Research is interested
in the use of entropy in an industrial domain. Business could use entropy of diagnoses
for developing very dependable medical systems, or in general, embedded systems.
• Currently, the log entries of the Philips Cardio-Vascular X-Ray System logs do not contain
sufficient data that can be used as observables in a MBD implementation. MBD and entropy
are suitable mechanisms to decide which variables should be added to the log, or which additional
sensors lead to the highest improvement of diagnostic performance. The approach for
achieving this consists of the following steps: construct the model of a subsystem. Perform an
entropy study to decide which measurements are best. If necessary, add sensors to the target
system. Implement the mechanism to log the chosen variables. During runtime operation,
the model and the logged observables must be fed to the MBD engine, resulting in a higher
diagnostic performance.
• The case study examined fault scenarios in which only one component was at false (single
fault). Fault scenarios in which a failure is caused by multiple faults are likely to occur in
reality, and implementing a MBD implementation that is able to accurately diagnose such
fault scenarios could lead to higher dependability of the target system.
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