Enabling data-driven condition monitoring of power electronic systems with artificial intelligence: Concepts, tools, and developments

Shuai Zhao, Huai Wang

    Research output: Contribution to specialist publicationArticle

    43 Scopus citations

    Abstract

    Condition monitoring is a proactive measure to 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 understanding 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.

    Original languageBritish English
    Pages18-27
    Number of pages10
    Volume8
    No1
    Specialist publicationIEEE Power Electronics Magazine
    DOIs
    StatePublished - Mar 2021

    Fingerprint

    Dive into the research topics of 'Enabling data-driven condition monitoring of power electronic systems with artificial intelligence: Concepts, tools, and developments'. Together they form a unique fingerprint.

    Cite this