Diagnosis interval estimation in process analysis for troubleShooting tasks

The paper presents an approach for dealing with the problems of process diagnosis interval estimation for troubleshooting tasks including the stochastic factors involved in the decision environment, and taking the issue of parameter variability relevance into consideration. It integrates Taguchi's deterministic approach for determining optimal diagnosis interval with the more traditional statistical decision making tools for use in troubleshooting investigations applicable to repair maintenance. Suggestions for extending these methods for decision making in preventive maintenance scheduling, as well as in other process control tasks are included.