Early Estimation of Faults in Induction Motors Using Symbolic Dynamic-Based Analysis of Stator Current Samples

Even though induction motors are frequently used electromagnetic devices in industries owing to their high reliability, high efficiency, and low maintenance requirements, they are prone to various faults and failures. Most of these faults occurring in the induction motors are perceptible in nascent stages. This averts the inopportune machine failures and helps to adeptly plan the maintenance schedules. Most of the methods used for preprediction of faults in induction motors are based on complicated techniques involving tortuous mathematical analysis. Although the importance and accuracy of these methods cannot be overruled, but a simple method is required as a first stage necessary condition test, which can classify the motor health condition into one of the three broad categories, namely, healthy, fault prone, and critical. This paper discusses a simple method based on symbolic dynamic analysis of stator current samples to detect faults in the induction motors. The experimentation has been performed on a 3 Φ, 1.5 kW, 4P, 1440 RPM squirrel cage motor to validate the proposed scheme. The data captured through the laboratory setup have been used to corroborate the proposed symbolic dynamic-based scheme.