Data-driven roller bearing diagnosis using degree of randomness and laplace test
Document Type
Conference Proceeding
Publication Date
1-1-2009
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.
Publication Source (Journal or Book title)
Annual Conference of the Prognostics and Health Management Society, PHM 2009
Recommended Citation
Ling, B., Khonsari, M., & Hathaway, R. (2009). Data-driven roller bearing diagnosis using degree of randomness and laplace test. Annual Conference of the Prognostics and Health Management Society, PHM 2009 Retrieved from https://repository.lsu.edu/mechanical_engineering_pubs/1363