• Process mining provides insight into how systems are actually used. This is interesting from a marketing point of view. For example, if a feature is rarely used, then this may trigger additional after sales activities. It is also possible that, based on process mining results, the feature is removed or adapted in future systems.
• Testing can be improved by constructing test scenarios based on the actual use of the machines. For instance, for medical equipment it is essential to prove that the system was tested under realistic circumstances.
• Process mining can be used to improve the reliability of next generations of systems. Better systems can be designed by understanding why and when systems malfunction.
• Process mining can also be used for fault diagnosis. By learning from earlier problems, it is possible to find the root cause for new problems that emerge. For example, we have analyzed under which circumstances particular components are replaced. This resulted in a set of signatures. When a malfunctioning X-ray machine exhibits a particular “signature” behavior, the service engineer knows what component to replace.
• Historic information can also be used to predict future problems. For instance, it is possible to anticipate that an X-ray tube is about to fail. Hence, the tube can be replaced before the machine starts to malfunction.
These examples show the potential of remote diagnostics based on process mining.
• Process mining provides insight into how systems are actually used. This is interesting from a marketing point of view. For example, if a feature is rarely used, then this may trigger additional after sales activities. It is also possible that, based on process mining results, the feature is removed or adapted in future systems. • Testing can be improved by constructing test scenarios based on the actual use of the machines. For instance, for medical equipment it is essential to prove that the system was tested under realistic circumstances. • Process mining can be used to improve the reliability of next generations of systems. Better systems can be designed by understanding why and when systems malfunction. • Process mining can also be used for fault diagnosis. By learning from earlier problems, it is possible to find the root cause for new problems that emerge. For example, we have analyzed under which circumstances particular components are replaced. This resulted in a set of signatures. When a malfunctioning X-ray machine exhibits a particular “signature” behavior, the service engineer knows what component to replace. • Historic information can also be used to predict future problems. For instance, it is possible to anticipate that an X-ray tube is about to fail. Hence, the tube can be replaced before the machine starts to malfunction.These examples show the potential of remote diagnostics based on process mining.
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