Rotor fault diagnosis of frequency inverter fed or line-connected induction motors using mutual information

Abstract

Characteristics like robustness, adaptability to several load conditions, and low costs of operation and maintenance are some of the reasons three-phase induction motors are ubiquitous in industrial applications. Even so, these machines are subjected to electrical and mechanical faults which can result in malfunction. The detection of incipient faults is the focus of several recent studies because they provide information for timely decisions to avoid unplanned stops of industrial processes. A common problem of induction motors is the presence of broken rotor bars. Most of the methods of condition monitoring and fault diagnosis focus on the usage of only one type of power supply: line-connected or fed by frequency inverters. Given this situation, we present an alternative technique for the detection of broken rotor bars of three-phase induction motors regardless of the type of power supply. Our approach is based on similarity measures of the stator current signals of two motor phases in order to extract the relevant features of such signals, which are then classified using three intelligent systems: artificial neural network, support vector machines, and k -nearest neighbors. We performed 3310 experimental tests with motors operating in steady state, with sinusoidal and non-sinusoidal power supply, and variations of voltage unbalance levels, supply frequency, and load torque. Classification accuracy rates over 92% of success were obtained in these tests, which validate the proposed approach.