Test Aspects of System Health State Monitoring

Abstract

System health monitoring is an integral concept that involves observing, evaluating, and adapting the system behavior under varying operating conditions. The data can be collected from embedded instruments throughout the lifetime. Various techniques, including machine learning, have to be used to analyze the data and adapt the underlying system behavior. At the same time, the behavior of modern devices is affected by different types of variations. In order to develop an efficient and precise health monitoring scheme, the underlying analysis and adaptation techniques must be robust even in the presence of those variations. This contribution explores various strategies for overcoming this challenge across the system stack.