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首页> 外文期刊>IEEE transactions on industrial informatics >Rotor Bar Fault Monitoring Method Based on Analysis of Air-Gap Torques of Induction Motors
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Rotor Bar Fault Monitoring Method Based on Analysis of Air-Gap Torques of Induction Motors

机译：基于感应电动机气隙转矩分析的转子故障诊断方法

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摘要

A robust method to monitor the operating conditions of induction motors is presented. This method utilizes the data analysis of the air-gap torque profile in conjunction with a Bayesian classifier to determine the operating condition of an induction motor as either healthy or faulty. This method is trained offline with datasets generated either from an induction motor modeled by a time-stepping finite-element (TSFE) method or experimental data. This method can effectively monitor the operating conditions of induction motors that are different in frame/class, ratings, or design from the motor used in the training stage. Such differences can include the level of load torque and operating frequency. This is due to a novel air-gap torque normalization method introduced here, which leads to a motor fault classification process independent of these parameters and with no need for prior information about the motor being monitored. The experimental results given in this paper validate the robustness and efficacy of this method. Additionally, this method relies exclusively on data analysis of motor terminal operating voltages and currents, without relying on complex motor modeling or internal performance parameters not readily available.
机译：提出了一种可靠的方法来监视感应电动机的运行状况。该方法结合贝叶斯分类器利用气隙扭矩曲线的数据分析来确定感应电动机的运行状况是正常还是故障。该方法通过从通过时步有限元（TSFE）方法建模的感应电动机生成的数据集或实验数据进行离线训练。这种方法可以有效地监视与训练阶段使用的电动机在框架/等级，额定值或设计上不同的感应电动机的运行状况。这样的差异可以包括负载转矩水平和工作频率。这归因于此处介绍的新颖的气隙扭矩归一化方法，该方法导致了独立于这些参数的电动机故障分类过程，并且不需要有关被监视电动机的先前信息。本文给出的实验结果验证了该方法的鲁棒性和有效性。此外，该方法仅依赖于电动机端子工作电压和电流的数据分析，而无需依赖复杂的电动机建模或不易获得的内部性能参数。

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