Data-Driven Roller Bearing Diagnosis Using Degree of Randomness and Laplace Test

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Published Mar 26, 2021
Bo Ling
Migma Systems, Inc., 1600 Providence Highway, Walpole, MA 02081
Michael Khonsari
Department of Mechanical Engineering, Louisiana State University, Baton Rouge, LA 70803
Ross Hathaway
NASA Dryden Flight Research Center, Edwards, CA 93524

Abstract

In this paper, we present a new diagnosis and prognosis method using the degree of randomness (DoR) measure and Laplace test procedure. The abnormal events are detected based on changes of randomness of vibration signals. The trend of randomness is resulted from faulty components such as roller bearings. We aim at the early detection of semi-failure events through the use of Laplace test statistic which measures the rate changes of abnormal event occurrence. Algorithms are data-driven and capable of making fault detections at its early stages. They have also been integrated into a real-time diagnosis system.

How to Cite

Ling, B. ., Khonsari, M. ., & Hathaway, R. . (2021). Data-Driven Roller Bearing Diagnosis Using Degree of Randomness and Laplace Test. Annual Conference of the PHM Society, 1(1). Retrieved from http://papers.phmsociety.org/index.php/phmconf/article/view/1723
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Keywords

bearings, detection

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Issue
Vol. 1 No. 1 (2009): Proceedings of the Annual Conference of the PHM Society 2009
Section
Technical Research Papers
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