A Generic Neurofuzzy Model-Based Approach for Detecting Faults in Induction Motors

Abstract

Many fault detection and diagnosis schemes are based on the concept of comparing the plant output with a model in order to generate residues. A fault is deemed to have occurred if the residue exceeds a predetermined threshold. Unfortunately, the practical usefulness of model-based fault detection schemes is limited because of the difficulty in acquiring sufficiently rich experimental data to identify an accurate model of the system characteristics. This paper aims at developing a generic neurofuzzy model-based strategy for detecting broken rotor bars, which is one of the most common type of faults that may occur in a squirrel-cage induction motor. A neurofuzzy model that captures the generic characteristics of a class of asynchronous motor is the key component of the proposed approach. It is identified using data generated by a simulation model that is constructed using information on the name plate of the motor. Customization for individual motors is then carried out by selecting the threshold for fault detection via an empirical steady-state torque-speed curve. Since data obtained from a practical motor are used to select the threshold and not to build a complete model, the objective of reducing the amount of experimental input-output data required to design a model-based fault detector may be realized. Experimental results are presented to demonstrate the viability of the proposed fault detection scheme.