MATEC Web of Conferences
Volume 16, 2014CSNDD 2014 - International Conference on Structural Nonlinear Dynamics and Diagnosis
|Number of page(s)||4|
|Published online||01 September 2014|
Fault detection and diagnosis in induction motor using artificial intelligence technique
LRP & LEA Labs. Electronics Department, Batna University, Boukhlouf road, 05000, Algeria
Induction machines play a vital role in industry and there is a strong demand for their reliable and safe operation. The online monitoring of induction motors is becoming increasingly important. The main difficulty in this task is the lack of an accurate analytical model to describe a faulty motor. Faults and failures of induction machines can lead to excessive downtimes and generate large losses in terms of maintenance and lost revenues, and this motivates the examination of on-line condition monitoring. The major difficulty is the lack of an accurate model that describes a fault motor. Moreover, experienced engineers are often required to interpret measurement data that are frequently inconclusive. A fuzzy logic approach may help to diagnose induction motor faults. In fact, fuzzy logic is reminiscent of human thinking processes and natural language enabling decisions to be made based on vague information.
© Owned by the authors, published by EDP Sciences, 2014
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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