The positive features of neural networks and fuzzy logic are combined together for the detection of stator inter-turn insulation and bearing wear faults in single-phase induction motor. The adaptive neural fuzzy inference systems (ANFISs) are developed for the detection of these two faults. These faults are created experimentally on a single-phase induction motor in the laboratory. The experimental data is generated for the five measurable parameters, viz, motor intakes current, speed, winding temperature, bearing temperature, and the noise of the machine. Earlier, the ANFIS fault detectors are trained for the two input parameters, i.e., speed and current, and the performance is tested. Later, the three remaining parameters are added and the five input ANFIS fault detector is trained and tested. It observed from the simulation results that the five input parameter system predicts more accurate results.Index Terms-Adaptive neural fuzzy inference systems (ANFISs), bearing wear, induction motor, winding insulation.

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Local instantaneous pressure signals obtained through a magneto-fluidized bed have been analyzed using both classical and advanced signal analysis methods, which can deliver the necessary information about the presence of the bubbling and turbulent flow pattern. The conventional signal processing tool such as autocorrelation and cross correlation were used as preliminary tools to analyze the data. Evaluation of the dominant bubble frequency was completed using the autocorrelation function and power spectral density function. Mutual information function was used to identify the periodicity and the predictability of the local instantaneous pressure signals. Since it does not demand any particular functional relationships between the data points, it is a better method (compared to autocorrelation function) for measuring the predictability of nonlinear systems.

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