Induction motors are used in industrial, commercial and residential applications because they have considerable merits over other types of electric motors. These motors are used in various operating stresses that give rise to faults. Most recurrent faults in induction motors are bearing faults, stator interturn faults and cracked rotor bars. Early detection of induction motor faults is crucial for their reliable and economical operation. This could be done by motor monitoring, incipient fault detection and diagnosis. In many situations, failure of critically loaded machine can shut down an entire industry process. This growing demand for high-quality and low-cost production has increased the need for automated manufacturing systems with effective monitoring and control capabilities. Condition monitoring and fault diagnosis of an induction motor are of great importance in the production line. It can reduce the cost of maintenance and risk of unexpected failures by allowing the early detection of catastrophic failures. This work documents experimental results for multiple fault detection in induction motors using signalprocessing and artificial neural network-based approaches. Motor line currents recorded under various fault conditions were analyzed using continuous wavelet transform. A feedforward neural network was used for fault characterization based on fault features extracted using continuous wavelet transform.
This paper addresses the development of new signal processing approach based on Hilbert transform to extract the fault features in number of induction motor conditions. The motor conditions considered are normal condition and motors with bearing defects like inner race, outer race, stator interturn faults and rotor bar crack. Present approach is based on extraction of envelopes of the stator currents by Hilbert Transform. Representative features like maximum and minimum value, mean, standard deviation and norm are obtained from current envelopes. These features are then used as input to Artificial Neural Network. Experimental results obtained show that diagnostic system using MLP neural network along with Hilbert Transform is capable of classifying multiple faults in induction motor with high accuracy recognition rate.
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