Abstract
Unplanned downtime caused by rotating machines results in a significant percentage of the total downtime in manufacturing plants. These downtimes cause significant loss of production time. While preventative maintenance helps minimize lot of these downtimes, it can’t predict the upcoming downtimes and prevent them from occurring. That gave birth to predictive maintenance. While various techniques are used for predictive maintenance, vibration analysis is one of them. In this study, data driven and expert validated diagnosis using vibration analysis for rotary machinery is used. The data is collected from a manufacturing site in Minnesota where 32 rotating assets, including motors and fans, are monitored with the help of sensors. This data is monitored by the reliability engineers and prediction is made for any maintenance work needed.
Advisor
Pawan Bhandai
Committee Member
Kuldeep Agarwal
Committee Member
Shaheen Ahmed
Date of Degree
2026
Language
english
Document Type
Thesis
Degree
Master of Science (MS)
Program of Study
Manufacturing Engineering Technology
Department
Automotive and Manufacturing Engineering Technology
College
Science, Engineering and Technology
Recommended Citation
Pandey, Y. (2026). Vibration-based fault diagnosis in industrial rotating machinery [Master’s thesis, Minnesota State University, Mankato]. Cornerstone: A Collection of Scholarly and Creative Works for Minnesota State University, Mankato. https://cornerstone.lib.mnsu.edu/etds/1596/
Rights Statement
