| Abstract |
During the last decade, soft computing (computational intelligence)
has attracted great interest from different regions
of research. In this paper, we review the recent developments
in the field of soft computing-based electric motor
fault diagnosis. Several typical motor fault diagnosis
schemes using neural networks, fuzzy logic, neural-fuzzy,
and genetic algorithms are presented with descriptive diagrams
as well as simplified algorithms. Their advantages
and disadvantages are compared and discussed. We demonstrate
that soft computing methods are promising in tackling
difficult fault detection problems. |
