<div>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.</div><div><br> </div><div>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.</div><div><br> </div> The work of this
study leverages the turbine generator data the Wade Utility Plant
records and stores, to justify equipment care and provide early error
detection at an in-house level. The research collects and aggregates
operational, monitoring and performance-based data for the turbine
generator in Microsoft Excel, creating a dashboard which visually
displays and statistically monitors variables for discrepancies. The
dashboard records ninety days of data, tracked hourly, determining
averages, extrema, and alerting the user as data approaches recommended
warning levels. Microsoft Excel offers a low-cost and accessible
platform for data collection and analysis providing an adaptable and
comprehensible collection of data from a turbine generator. The
dashboard offers visual trends, simple statistics, and status updates
using 90 days of user selected data. This dashboard offers the ability
to forecast maintenance needs, plan work outages, and adjust operations
while continuing to provide reliable services that meet Purdue
University's utility demands. <br>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/17155994 |
| Date | 19 December 2021 |
| Creators | Emily R Rada (11813852) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/Turbine_Generator_Performance_Dashboard_for_Predictive_Maintenance_Strategies/17155994 |
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