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Comparative Analysis of Bearing Health Monitoring Methods for Machine Tool Linear Axes

Published

Author(s)

Jordan Jameson, Gregory W. Vogl

Abstract

The study of rotating machinery ball bearing diagnostics and prognostics is quite mature and an abundance of methods/algorithms are available to perform these functions. However, extending these algorithms to other ball bearing applications is challenging and may not yield usable results. A linear axis testbed was used to study the ability of an inertial measurement unit to measure changes in geometric error motions. Faults were introduced on the recirculating ball bearings of one carriage truck with increasing severity. The inertial measurement unit data was analyzed using a variety of methods proposed and used in the rotating machinery community, including auto-regressive filtering, self-adaptive noise cancellation, minimum entropy deconvolution, and spectral kurtosis. The results reveal a surprising ineffectiveness of the methods for bearing faults having low signal-to-noise ratio and/or weaker periodicity than faults in rotating machinery.
Proceedings Title
Society for Machinery Failure Prevention Technology 2018 Annual Conference
Conference Dates
May 15-17, 2018
Conference Location
Virginia Beach, VA, US

Keywords

Linear axis, ball bearing, degradation, diagnostics, smart manufacturing

Citation

Jameson, J. and Vogl, G. (2018), Comparative Analysis of Bearing Health Monitoring Methods for Machine Tool Linear Axes, Society for Machinery Failure Prevention Technology 2018 Annual Conference, Virginia Beach, VA, US, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=925909 (Accessed April 19, 2024)
Created May 16, 2018, Updated April 11, 2022