IEEE Trans Neural Netw Learn Syst. 2012 Jan;23(1):97-108. doi: 10.1109/TNNLS.2011.2178443.
In this paper, a novel approach to detect and classify comprehensive fault conditions of induction motors using a hybrid fuzzy min-max (FMM) neural network and classification and regression tree (CART) is proposed. The hybrid model, known as FMM-CART, exploits the advantages of both FMM and CART for undertaking data classification and rule extraction problems. A series of real experiments is conducted, whereby the motor current signature analysis method is applied to form a database comprising stator current signatures under different motor conditions. The signal harmonics from the power spectral density are extracted as discriminative input features for fault detection and classification with FMM-CART. A comprehensive list of induction motor fault conditions, viz., broken rotor bars, unbalanced voltages, stator winding faults, and eccentricity problems, has been successfully classified using FMM-CART with good accuracy rates. The results are comparable, if not better, than those reported in the literature. Useful explanatory rules in the form of a decision tree are also elicited from FMM-CART to analyze and understand different fault conditions of induction motors.
本文提出了一种使用混合模糊最大最小（FMM）神经网络和分类回归树（CART）来检测和分类感应电动机综合故障状态的新方法。这种称为 FMM-CART 的混合模型利用了 FMM 和 CART 的优势，用于进行数据分类和规则提取问题。进行了一系列实际实验，其中应用了电机电流特征分析方法来形成一个数据库，该数据库包含在不同电机条件下的定子电流特征。从功率谱密度中提取信号谐波作为 FMM-CART 进行故障检测和分类的判别输入特征。使用 FMM-CART 成功地对感应电动机的各种故障状态（如转子断条、不平衡电压、定子绕组故障和偏心问题）进行了分类，具有较高的准确率。这些结果与文献中的报告相当，甚至更好。还从 FMM-CART 中引出了有用的决策树形式的解释性规则，以分析和理解感应电动机的不同故障状态。
