Paper Title:
Machine Fault Diagnosis Using MLPs and RBF Neural Networks
  Abstract

- Fault detection and elimination in industrial machineries can help prevent loss of life and financial assets. In this study four common faults in rotating machineries namely: 1) Mass Unbalance 2) Angular Misalignment 3) Bearing Faults and 4) Mechanical Looseness have been considered. Each of these defects has been created separately on a test rig comprising of an electrical motor coupled to a rotor assembly. A Vibrotest 60 vibration spectrum analyzer has been used to collect velocity spectrum of the vibration on the bearings. Eleven characteristic features have been chosen to distinguish different faults. Based on the acquired data an Artificial Neural Network Multi Layer Perceptrons (MLPs) and Radial Basis Functions (RBF) Neural Network has been designed to recognize each one of the aforementioned defects. After training the Neural Network, it was checked by new data gathered by new experiments and the results showed that the designed network can predict the faults with more than 75% reliability, and it can be a good assistance to an ordinary machine operator to guess the problem and hence make a good decision.

  Info
Periodical
Chapter
Chapter 23: Computer-Aided Design, Manufacturing, and Engineering
Edited by
Wu Fan
Pages
5021-5028
DOI
10.4028/www.scientific.net/AMM.110-116.5021
Citation
G. H. Payganeh, M. N. Khajavi, R. Ebrahimpour, E. Babaei, "Machine Fault Diagnosis Using MLPs and RBF Neural Networks", Applied Mechanics and Materials, Vols. 110-116, pp. 5021-5028, 2012
Online since
October 2011
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Price
$32.00
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