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首页> 外文期刊>Journal of ambient intelligence and humanized computing >Detection and classification of mechanical faults of three phase induction motor via pixels analysis of thermal image and adaptive neuro-fuzzy inference system
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Detection and classification of mechanical faults of three phase induction motor via pixels analysis of thermal image and adaptive neuro-fuzzy inference system

机译：三相感应电动机机械故障的检测与分类热图像和自适应神经模糊推理系统的像素分析

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摘要

The fault diagnosis in motor is substantial as it results in breakdown of production line and the faults may damage motor and results in economic losses. Bearing failure, rotor eccentricity, shaft misalignment and load related faults are the most frequent failures under mechanical fault category. This paper addresses three such faults that may increase the stator temperature namely air gap eccentricity, shaft misalignment and cooling system failure. The thermography technique has been used widely for fault detection in induction motor. In three phase induction motor the thermal images are analyzed for healthy condition and the above mentioned faults conditions. This paper presents thermal pixels counting algorithm to calculate the diagnosis indicators and adaptive neuro-fuzzy inference system classifier is used to classify the faults based on the diagnosis indicator data base. Laboratory based experimental investigation are carried out to verify the accuracy of the proposed method. This method provides accurate diagnosis indicator that will be used as a bench mark value for preparing the maintenance schedule under non-destructive mode.
机译：电动机故障诊断很大，因为它导致生产线的故障，故障可能会损坏电机并导致经济损失。轴承故障，转子偏心，轴未对准和负载相关的故障是机械故障类别中最常见的故障。本文解决了三种可能会增加定子温度即气隙偏心，轴未对准和冷却系统故障的这种故障。热成像技术广泛用于感应电动机中的故障检测。在三相感应电动机中，分析热图像以进行健康状况和上述故障条件。本文介绍了计算诊断指示符和自适应神经模糊推理系统分类器的热像素计数算法，用于基于诊断指示数据库对故障进行分类。进行了实验室的实验研究，以验证所提出的方法的准确性。该方法提供了准确的诊断指示器，将用作在非破坏性模式下准备维护计划的基准标记值。

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