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    Area of Science:

    • Electrical Engineering
    • Data Science
    • Machine Learning

    Background:

    • Automated defect detection in electric motors is crucial for reliability.
    • Limited real-world failure data hinders data-driven defect detection methods.
    • Simulated data lacks real-world complexity, impacting model performance.

    Purpose of the Study:

    • To introduce a visual analysis tool for comparing measured and simulated electric motor data.
    • To identify domain-invariant features and assess simulation accuracy.
    • To aid in selecting optimal training data for robust automated defect detection.

    Main Methods:

    • Development of a visual analysis tool for time-series data.
    • Application of visual design principles tailored for electric motor professionals.
    • Validation through a think-aloud study with specialized engineers.

    Main Results:

    • The tool facilitates the identification of discrepancies between real and simulated motor data.
    • It aids in understanding simulation data limitations for defect detection.
    • The visual design effectively supports engineers in data selection.

    Conclusions:

    • The proposed visual analysis tool enhances the reliability of automated defect detection systems for electric motors.
    • It bridges the gap between simulated and real-world data challenges.
    • The tool's design, validated by domain experts, meets practical engineering needs.