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Bearing Metrics for Health Monitoring of Machine Tool Linear Axes



Gregory W. Vogl, Brian C. Galfond, Jordan Jameson


Diagnostics and prognostics of rotating machinery ball bearings is quite mature with an abundance of available methods and algorithms. However, extending these algorithms to other ball bearing applications is challenging and may not yield usable results. This work used a linear axis to study the ability of an inertial measurement unit (IMU), along with nine signal features, to measure changes in geometric error motions due to induced faults on the recirculating ball bearings of two carriage trucks. The IMU data was analyzed with the nine features used for rotating machinery systems, including root-mean-square, standard deviation, and kurtosis. For each stage of degradation, the statistical population and median value of each feature were compared against the population and median for no degradation, to monitor feature changes due to ball damage. Correlation analyses revealed an ability of the standard deviation feature to detect statistically significant changes as small as 0.05 micrometers or 0.5 microradians, corresponding to a total damaged surface area of truck balls of less than 0.1 percent.
Proceedings Title
2019 Manufacturing Science and Engineering Conference (MSEC 2019)
Conference Dates
June 10-14, 2019
Conference Location
Erie, PA, US


machine tool, smart manufacturing, linear axis, ball bearing, wear, degradation, diagnostics


Vogl, G. , Galfond, B. and Jameson, J. (2019), Bearing Metrics for Health Monitoring of Machine Tool Linear Axes, 2019 Manufacturing Science and Engineering Conference (MSEC 2019), Erie, PA, US, [online], (Accessed April 18, 2024)
Created June 13, 2019, Updated April 14, 2022