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Shaft Crack Detection Using Statistical Analysis

[+] Author Affiliations
Zhuang Li, Lei Jin, Ning Zhang, Yang Zhou

McNeese State University, Lake Charles, LA

Paper No. IMECE2009-12487, pp. 429-434; 6 pages
  • ASME 2009 International Mechanical Engineering Congress and Exposition
  • Volume 15: Sound, Vibration and Design
  • Lake Buena Vista, Florida, USA, November 13–19, 2009
  • Conference Sponsors: ASME
  • ISBN: 978-0-7918-4388-8 | eISBN: 978-0-7918-3863-1
  • Copyright © 2009 by ASME


Cracks and voids are common defects in rotating systems and are a precursor to fatigue-induced failure. The application of statistical analysis, as a tool for damage identification and health monitoring in rotating machinery, is investigated. Experimental vibration data were collected for a set of health and cracked shafts. Formal statistical models have been proposed to describe the relationship between the vibration signals and the existence of damage. Damage detection and diagnosis are implemented based on statistical estimation and hypothesis testing. Such a statistical model provides a screening technique to detect other damage types. As a result, the proposed methods can improve the power of damage detection.

Copyright © 2009 by ASME



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