Condition monitoring is a proactive measure to conditions, degradation level, remaining useful lifetime realize operation optimization, predictive maintenance, and high availability of Power Electronic Systems (PES). It is demanded by reliability-, safety-, or availability-critical applications. The core of condition monitoring is a prediction based on historical and present information. Artificial Intelligence (AI) could play a role in addressing optimization, regression, and classification problems in predicting the operation or health status of PES. Besides AI algorithms, quality data collection, objective formulation, and result validation require an in-depth under- standing of the PES. The nexus between PES and AI expects to create overarching effects in the condition monitoring area. This article presents exploratory efforts in the data-driven condition monitoring of PES in the view of existing challenges and emerging opportunities.
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