Authors : M. Sreedevi and P. Jeno Paul
Abstract: Many researches dealt with the problem of induction motors fault detection and diagnosis. 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 neuro fuzzy logic approach may help to diagnose induction motor faults. In fact, neuro fuzzy logic is reminiscent of human thinking processes and natural language enabling decisions to be made based on vague information. Therefore, this study applies fuzzy logic to induction motors fault detection and diagnosis. The motor condition is described using linguistic variables. Neuro fuzzy subsets and the corresponding membership functions describe stator current amplitudes. A knowledge base, comprising rule and data bases is built to support the fuzzy inference. The induction motor condition is diagnosed using a compositional rule of fuzzy inference using Matlab/ Simulink.
M. Sreedevi and P. Jeno Paul, 2011. Stator Fault Detection and Diagnosis of a Induction Motor Using Neuro Fuzzy Logic. International Journal of Electrical and Power Engineering, 5: 102-107.