Condition monitoring reduces maintenance costs on industrial machinery by reducing downtime and allowing for need-based maintenance instead of schedule-based maintenance. Currently, condition monitoring is not as widely applied on reciprocating compressors as compared to rotating compressors. However, research for monitoring various components of reciprocating compressors such as inlet and outlet valves and piston rings is conducted. There is industry interest into expanding this research to the main bearings of the compressor. Previous research on bearings focuses on either rolling element bearings or traditional journal bearings with not much information available on low speed applications of fully floating ring journal bearings as are studied in this work. The following work shows a detailed derivation of the forces acting on the main bearings during normal compressor operation based on kinematic relations and dynamic equivalence. The bearing is simulated using an adaptation of the mobility method for fully floating ring bearings found in previous research. It involves solving two simultaneous mobility calculations along with the ring speed to link the inner to the outer bearing. Experimental data of the crankshaft orbit is collected for comparison to the simulation. Condition monitoring for three different fault types is investigated through seeded fault testing: Varying lubricant viscosity, oil feed hole obstruction, and grooves in the bearing land. Principle component analysis has been shown previously to be a successful method of feature selection for classification. This is applied to several sensors and the classification results are compared. A single axis position measurement of the crankshaft shows the most promising results compared to a traditional accelerometer on the bearing housing and a novel accelerometer on the crankshaft. The single axis measurement provides a cost efficient alternative method to the two axis orbit measurement typically used for traditional journal bearings.
Library of Congress Subject Headings
Bearings (Machinery)--Reliability--Data processing; Compressors--Testing--Data processing; Classification--Data processing
Department, Program, or Center
Mechanical Engineering (KGCOE)
Holzenkamp, Markus, "Modeling and condition monitoring of fully floating reciprocating compressor main bearings using data driven classification" (2013). Thesis. Rochester Institute of Technology. Accessed from
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