This article presents a two-stage fault detection and classification scheme specifically designed for rotating electrical machines. The approach involves the use of new condition indicators that are specific to the frequency domain. The paper proposes two distinct features: one based on the extraction of peaks by using the prominence measure, a technique originating from the topology of mountains, and other based on the calculation of the occupied band power ratio for specific characteristic fault frequencies. A linear based feature reduction technique, the principal component analysis (PCA) has been employed to represent all the data. Afterwards, shallow neural networks have been used to detect and classify the three-phase current signals online. The effectiveness of the proposed scheme has been validated experimentally by using signals obtained with grid and inverter fed induction motors.

中文翻译：

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使用非参数统计频率特征和浅层神经网络的感应电机故障检测和分类

本文提出了一种专为旋转电机设计的两阶段故障检测和分类方案。该方法涉及使用特定于频域的新状态指示器。本文提出了两个不同的特征：一个基于基于突出测量的峰值提取，一种源自山脉拓扑的技术，另一个基于对特定特征故障频率的占用频带功率比的计算。基于线性的特征约简技术，即主成分分析（PCA）已被用来表示所有数据。之后，浅层神经网络已被用于在线检测和分类三相电流信号。