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Online Motor Fault Detection and Diagnosis Using a Hybrid FMM-CART Model

机译:使用混合FMM-CART模型的在线电动机故障检测和诊断

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In this brief, a hybrid model combining the fuzzy min-max (FMM) neural network and the classification and regression tree (CART) for online motor detection and diagnosis tasks is described. The hybrid model, known as FMM-CART, exploits the advantages of both FMM and CART for undertaking data classification and rule extraction problems. To evaluate the applicability of the proposed FMM-CART model, an evaluation with a benchmark data set pertaining to electrical motor bearing faults is first conducted. The results obtained are equivalent to those reported in the literature. Then, a laboratory experiment for detecting and diagnosing eccentricity faults in an induction motor is performed. In addition to producing accurate results, useful rules in the form of a decision tree are extracted to provide explanation and justification for the predictions from FMM-CART. The experimental outcome positively shows the potential of FMM-CART in undertaking online motor fault detection and diagnosis tasks.
机译:在本文中,描述了一种混合模型,该模型将模糊最小-最大值(FMM)神经网络与分类和回归树(CART)结合在一起,用于在线运动检测和诊断任务。称为FMM-CART的混合模型利用FMM和CART的优势进行数据分类和规则提取问题。为了评估所提出的FMM-CART模型的适用性,首先进行了与基准数据集有关的电动机轴承故障的评估。获得的结果与文献报道的结果相同。然后,进行用于检测和诊断感应电动机中的偏心故障的实验室实验。除了产生准确的结果外,还提取决策树形式的有用规则,以为来自FMM-CART的预测提供解释和依据。实验结果积极地表明了FMM-CART在进行在线电动机故障检测和诊断任务方面的潜力。

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