Year
2026
Season
Spring
Paper Type
Master's Thesis
College
College of Computing, Engineering & Construction
Degree Name
Master of Science in Mechanical Engineering (MSME)
Department
Engineering
NACO controlled Corporate Body
University of North Florida. School of Engineering
Committee Chairperson
Dr. Gökan May
Second Advisor
Dr. Longfei Zhou
Rights Statement
http://rightsstatements.org/vocab/InC/1.0/
Third Advisor
Dr. Nelson Delfino de Campos Neto
Department Chair
Dr. Alan Harris
College Dean
Dr. William Klostermeyer
Abstract
A Modern American Manufacturing resurgence has been delayed due to economic factors. Techniques like those defined by the data driven methodologies of Industry 4.0 promise to facilitate resurgence through reduced cost. It can be expensive to modernize manufacturing capacity with the sensors needed to provide the data required for real time monitoring and advanced preventative maintenance. A popular technology for preventative maintenance was with a technique called Motor Current Signal Analysis. The data were often acquired by installing sensors within the machine to isolate data to given components. The use of Motor Current Signal Analysis as a common tool for diagnosing faults in a system suggested that a “Current Only” Total Input Current Signal Analysis could be sufficient to determine an array of complex behaviors by looking only at the input current.
This Thesis designed, built, and tested a novel custom current sensing device to determine the physical state of the plant in terms of its active Geometry and Mechanical Codes. Once validated against a traceable standard, the device was used on an actual Manufacturing Plant where an experiment was performed to extract the features of the relevant Geometry and Mechanical Codes at a sample rate of 70kHz over four channels and thirty minutes of data. A series of conventional and novel analysis techniques were pursued to compare operations and determine their detectability. Transience could be determined with a power deviation algorithm and using a Lumped Cross-Correlation analysis. It was determined that a basic algorithm using four statistically independent tests could correctly detect the output at a minimum of rate of 27%, 72%, 72% and 94% for one, two, three and four tests true respectively, while using a rectangular window and a 50% sample overlap.
Suggested Citation
Carter, Matthew Scott, "An analysis of the repeatability of power consumption in industrial machinery" (2026). UNF Graduate Theses and Dissertations. 1425.
https://digitalcommons.unf.edu/etd/1425
Included in
Applied Mechanics Commons, Computer and Systems Architecture Commons, Controls and Control Theory Commons, Data Storage Systems Commons, Electrical and Electronics Commons, Electro-Mechanical Systems Commons, Manufacturing Commons, Other Engineering Commons, Other Operations Research, Systems Engineering and Industrial Engineering Commons, Power and Energy Commons, Signal Processing Commons, Systems and Communications Commons
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