Applying Natural Language Processing and Machine Learning to Support Underground Transmission Asset Management by Analysis of Maintenance Records
A scalable, data-driven approach using natural language processing (NLP) and machine learning (ML) enhances underground (UG) transmission asset management by unlocking the latent value in historical maintenance work order records to improve reliability, mitigate risk, and support cost-effective asset stewardship.