Improved Processing and Analysis of Test and Operating Data from Rotating Machines [2024 INV#DOI-62]
Description
This research strives to aid in the development of condition-based maintenance (CBM) and predictive maintenance (PdM) tools for Hydroelectric Facilities (Generators and Pumps) by exploring, testing, and developing software tools to process data collected from rotating machines. These software tools use various forms of AI/ML.
Detailed example
Data-driven tools using AI/ML techniques to inform power plant maintenance.
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
a) Pre-deployment – The use case is in a development or acquisition status.
Expected benefit
CBM and PdM aim to increase hydropower generation by reducing outages for maintenance and lower O&M costs by only performing maintenance when needed.
Controls / human review
ATO: No; PIA: Not published