Evolving neural fuzzy classifier for machinery diagnostics / by Ofelia Antonia Jianu.

Abstract

"The classical techniques for fault diagnosis require periodic shut down of machines for manual inspection. Although these techniques can be used for fault diagnosis in simple machines, they can rarely be used effectively for complex ones. Due to the rapid growing market competitiveness, more reliable and robust condition monitoring systems are critically needed in a wide array of industries to improve production quality and reduce cost. As a result, in recent years more efforts have been taken to develop intelligent techniques for online condition monitoring in machinery systems. Several neural fuzzy classification schemes have been proposed in literature for fault detection. However, the reasoning architecture of the classical neural fuzzy classifiers remains fixed, allowing only the system parameters to be updated in pattern classification operations. To improve the reliability of machinery fault diagnostics, an evolving fuzzy classifier is developed in this work for gear system condition monitoring.