Rada-Thesis Final 12.8-edits.pdf (922.18 kB)
Turbine Generator Performance Dashboard for Predictive Maintenance Strategies
Equipment health is the root of productivity and profitability in a
company; through the use of machine learning and advancements in
computing power, a maintenance strategy known as Predictive Maintenance
(PdM) has emerged. The predictive maintenance approach utilizes
performance and condition data to forecast necessary machine repairs.
Predicting maintenance needs reduces the likelihood of operational
errors, aids in the avoidance of production failures, and allows for
preplanned outages. The PdM strategy is based on machine-specific data,
which proves to be a valuable tool. The machine data provides
quantitative proof of operation patterns and production while offering
machine health insights that may otherwise go unnoticed.
Purdue
University's Wade Utility Plant is responsible for providing reliable
utility services for the campus community. The Wade Utility Plant has
invested in an equipment monitoring system for a thirty-megawatt turbine
generator. The equipment monitoring system records operational and
performance data as the turbine generator supplies campus with
electricity and high-pressure steam. Unplanned and surprise maintenance
needs in the turbine generator hinder utility production and lessen the
dependability of the system.
History
Degree Type
- Master of Science
Department
- Engineering Technology
Campus location
- West Lafayette
