A decision support model for determining the applicability of prognostic health management (PHM) approaches to electronic systems

This paper presents a model that enables the determination of when scheduled maintenance makes sense, and how to optimally interpret prognostic health management (PHM) results for electronic systems. In this context, optimal interpretation of PHM results means translating PHM information into maintenance policies that minimize life cycle costs. The electronics PHM problem is characterized by imperfect and partial monitoring and a significant random/overstress failure component must be considered in the decision process. Specifically the model enables determining on an application-specific basis when the reliability of electronics has become predictable enough to warrant the application of PHM-based scheduled maintenance concepts. Given that the forecasting ability of PHM (whether health monitoring or life consumption monitoring based) is fraught with uncertainties in the sensor data collected, the models applied, the material parameters assumed in the models, etc., the model in this paper addresses how PHM results can be interpreted so as to provide value to the system. The result of the model is the determination of optimal safety margins on life consumption monitoring predictions and prognostic distances for health monitoring. The model provides the type of information needed to construct a business case showing the application-specific usefulness of health monitoring and/or life consumption monitoring for electronic systems.