A Primer on Vibrational Ball Bearing Feature Generation for Prognostics and Diagnostics Algorithms

Report No. ARL-TR-7230
Authors: Kwok F Tom
Date/Pages: March 2015; 54 pages
Abstract: This report is the result of a prognostic and diagnostic program involving roller bearings. The objective of the effort was to develop techniques that could be used to detect the initial fault and predict the remaining useful life of a roller bearing. There are many techniques from digital signal processing, statistical, and machine learning fields that can be for fault detection and prediction. In this report, a description of roller bearing faults and life are presented. From this starting point, the report leads into various techniques that can be applied to vibrational data in order to generate features that can be used for fault detection. Feature generation is an important step in the prognostic and diagnostic development. This overview of possible features is intended to provide sufficient information to pursue feature selection and algorithm development for roller bearings prognostic and diagnostic techniques.
Distribution: Approved for public release
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Last Update / Reviewed: March 1, 2015