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

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Published Mar 26, 2021
Bo Ling Michael Khonsari Ross Hathaway

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 https://papers.phmsociety.org/index.php/phmconf/article/view/1723
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Keywords

bearings, detection

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Section
Technical Papers