Aplicação e comparação de técnicas de diagnóstico e detecção de falhas em motores elétricos de indução baseados em assinatura de corrente

Abstract

The induction motors are used worldwide in various industries. Several maintenance techniques are applied to increase the operating time and the lifespan of these motors. Among these, the predictive maintenance techniques such as Motor Current Signature Analysis (MCSA), Motor Square Current Signature Analysis (MSCSA), Park's Vector Approach (PVA) and Park's Vector Square Modulus (PVSM) are used to detect and diagnose faults in electric motors, characterized by patterns in the stator current frequency spectrum. In this work, these techniques are applied and compared on real motors, which have the faults of eccentricity in the air-gap, inter-turn short circuit and broken bars. It was used a theoretical model of an electric induction motor without fault and with the same voltage supply in order to assist comparison between the stator current frequency spectrum patterns with and without faults. Metrics were purposed and applied to evaluate the sensitivity of each technique fault detection. The results presented here show that the above techniques are suitable for the faults above mentioned.