Automating motor data collection with machine learning : object detection and optical character recognition for data acquisition
Ruotsalainen, Ville (2025)
2025
Sähkötekniikan DI-ohjelma - Master's Programme in Electrical Engineering
Informaatioteknologian ja viestinnän tiedekunta - Faculty of Information Technology and Communication Sciences
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Hyväksymispäivämäärä
2025-03-21
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tuni-202503192886
https://urn.fi/URN:NBN:fi:tuni-202503192886
Tiivistelmä
Reliable data of installed electric motors is critical for maintaining productivity and reliability in industrial environments. ABB’s current on site motor data collection processes rely on manual data entry, which is time-intensive and susceptible to errors, limiting scalability and accuracy. This thesis investigates the automation of motor data collection through the integration of advanced object detection and optical character recognition technologies, focusing on standard IEC low voltage induction motors. The research leverages state-of-the-art machine learning models: YOLOv11 for object detection and TrOCR for optical character recognition, to automate the identification and transcription of motor nameplate data.
Trained with ABB’s annotated nameplate data YOLOv11 demonstrated strong performance in detecting key attributes such as type codes, product codes, and serial numbers. TrOCR, utilizing a transformer-based architecture, provided reliable text recognition, enabling accurate extraction of critical alphanumeric data. Combined, these models showcased significant potential to replace manual workflows, streamline data acquisition, and enhance ABB’s data management processes.
Testing on unseen data confirmed the system’s ability to generalize effectively, although challenges such as class imbalance and variability in orientation of technical attributes remain. Future work could address these challenges by expanding the dataset to include diverse orientations and training the models in cloud environment with better computing power. The findings suggest significant benefits for ABB, including reduced manual workload, improved data reliability, and enhanced scalability in managing large motor inventories.
This research demonstrates the transformative potential of machine learning in industrial applications, providing a foundation for further advancements in digital asset management and predictive maintenance. By automating motor data collection, ABB can improve operational efficiency and service quality, contributing to a broader digital transformation in industrial maintenance workflows.
Trained with ABB’s annotated nameplate data YOLOv11 demonstrated strong performance in detecting key attributes such as type codes, product codes, and serial numbers. TrOCR, utilizing a transformer-based architecture, provided reliable text recognition, enabling accurate extraction of critical alphanumeric data. Combined, these models showcased significant potential to replace manual workflows, streamline data acquisition, and enhance ABB’s data management processes.
Testing on unseen data confirmed the system’s ability to generalize effectively, although challenges such as class imbalance and variability in orientation of technical attributes remain. Future work could address these challenges by expanding the dataset to include diverse orientations and training the models in cloud environment with better computing power. The findings suggest significant benefits for ABB, including reduced manual workload, improved data reliability, and enhanced scalability in managing large motor inventories.
This research demonstrates the transformative potential of machine learning in industrial applications, providing a foundation for further advancements in digital asset management and predictive maintenance. By automating motor data collection, ABB can improve operational efficiency and service quality, contributing to a broader digital transformation in industrial maintenance workflows.