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A Practical Method for Multi-Fault Diagnosis of Rolling Element Bearings Using Discrete Wavelet Transform and Support Vector Machine

[+] Author Affiliations
Saeed Abbasion, Mohamad Bashari

Iran University of Science and Technology

Anoushiravan Farshidianfar

Ferdowsi University of Mashhad

Nilgoon Irani

Amirkabir University of Technology

Paper No. IMECE2006-14211, pp. 369-378; 10 pages
doi:10.1115/IMECE2006-14211
From:
  • ASME 2006 International Mechanical Engineering Congress and Exposition
  • Innovations in Engineering Education: Mechanical Engineering Education, Mechanical Engineering Technology Department Heads
  • Chicago, Illinois, USA, November 5 – 10, 2006
  • Conference Sponsors: Mechanical Engineering Education, Mechanical Engineering Technology Department Heads
  • ISBN: 0-7918-4781-0 | eISBN: 0-7918-3790-4
  • Copyright © 2006 by ASME

abstract

Due to importance of rolling bearings as one of the most widely used industrial machinery elements, development of proper monitoring and fault diagnosis procedure to prevent malfunctioning and failure of these elements during operation is necessary. For rolling bearing fault detection, it is expected that a desired time-frequency analysis method have good computational efficiency, and have good resolution in both, time and frequency domain. The point of interest in this investigation is the present of an effective method for multi fault diagnosis in such systems with optimizing signal decomposition levels by using wavelet analysis and support vector machine (SVM). The system that is under study is an electric motor which has two rolling bearings, one of them is next to the output shaft and the other one is next to the fan and for each of them there is one normal form and three false forms, which make 8 forms for study. The outcome that we have achieved from wavelet analysis and SVM are fully in agreement with empirical result.

Copyright © 2006 by ASME

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