AI-Powered Identification of Industrial Nameplates

Use Case:

Automation of nameplate recognition in chemical plant environments accelerating maintenance workflows and minimizing manual input and errors.

Outcome:

Prototype validated for field use (Technology Readiness Level 6); 50% reduction in time for asset identification; Increased robustness and accuracy of OCR under challenging field conditions; Business model validated for service-based and product-based deployment; UX improvement cycle initiated with customer pilots and expert workshops.

Ecosystem Support:

Supported through the StairwAI program with access to mentoring (business and technical), OCR expertise, and application development coaching.

AI Relevance:

This success story demonstrates the value of democratized AI in industrial settings through: I) low-threshold AI adoption for SMEs; II) seamless integration of OCR and NLP for real-world asset recognition; III) product modularity supporting both SaaS and stand-alone software delivery; IV) enhanced maintenance traceability and digitalization in critical infrastructure sectors.

Summary:

TUEG Schillings GmbH, a German SME active in the industrial services sector, developed AssetCollector to tackle a persistent challenge in chemical plants: recognizing and cataloging nameplates attached to industrial assets. These plates, often dirty, corroded, or installed in low-visibility conditions, posed significant delays to maintenance operations and asset tracking. The company developed a working prototype that leverages Optical Character Recognition (OCR) and AI-enhanced image processing to interpret sequences of letters and numbers on nameplates—even in challenging visual conditions. The system identifies contrasts, edges, and contours to accurately extract relevant data, assisted by lightweight natural language models to validate and structure the readings. AssetCollector was designed to be modular: it can be deployed either as a service offered directly by TUEG’s in-house electricians or as a standalone software solution that customers can operate independently. This flexibility broadens market reach, offering time-saving benefits both for large industrial clients and smaller maintenance teams. Throughout the StairwAI program, the team advanced the solution to TRL 6, launched initial customer pilot studies, and began a UX optimization process through expert-led workshops. Their work sets the stage for broader AI adoption in maintenance operations and supports the broader European agenda for AI-driven industrial digitalization.