LAUSR

Squirrel-cage induction motors are widely used in a number of applications throughout the world. This paper proposes a neuro-fuzzy approach to identify and to classify a typical fault related to the induction motor damage, such as broken rotor bars. Two fuzzy classifiers are obtained by an adaptive-network-based fuzzy inference system model whose parameters can be identified by using the hybrid learning algorithm. A Hall effect sensor was installed between two stator slots of the induction machine, and a magnetic flux density variation is measured according to the failure. The data from the Hall sensor were used to extract some harmonic components by applying fast Fourier transform. Thus, some frequencies and their amplitudes were considered as inputs for the proposed fuzzy model to detect not only adjacent broken bars, but also noncontiguous faulted scenarios. In the present work it is not necessary to estimate the rotor slip, as required by the traditional condition monitoring technique, known as motor current signature analysis. This method was able to detect broken bars for induction motor running at low-load or no-load condition. The intelligent approach was validated using some experimental data from a 7.5-kW squirrel-cage induction machine.