The Permanent Magnet Synchronous Motor (PMSM) is the power source maintaining the stable and efficient operation of various pieces of equipment; hence, its reliability is crucial to the safety of public equipment. Convolutional Neural Network (CNN) models face challenges in extracting features from PMSM current data. A new Discrete Wavelet Transform Convolutional Neural Networks (DW-CNN) feature with fusion weight updating Long Short-Term Memory (LSTM) anomaly detection is proposed in this paper. This approach combines Discrete Wavelet Transform (DWT) with high and low-frequency separation processing and LSTM. The anomaly detection method adopts DWT and CNN by separating high and low-frequency processing. Moreover, this method combines the hybrid attention mechanism to extract the multi-current signal features and detects anomalies based on weight updating the LSTM network. Experiments on the motor bearing real fault dataset and the PMSM stator fault dataset prove the method’s strong capability in fusing current features and detecting anomalies.

中文翻译：

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基于改进DWT-CNN多电流融合的永磁同步电机异常检测

永磁同步电机（PMSM）是维持各种设备稳定、高效运行的动力源；因此，其可靠性对于公共设备的安全至关重要。卷积神经网络 (CNN) 模型在从 PMSM 当前数据中提取特征方面面临挑战。本文提出了一种新的离散小波变换卷积神经网络（DW-CNN）特征，具有融合权重更新长短期记忆（LSTM）异常检测功能。该方法将离散小波变换 (DWT) 与高频和低频分离处理以及 LSTM 相结合。异常检测方法采用DWT和CNN，高低频分离处理。此外，该方法结合混合注意力机制来提取多电流信号特征，并基于权值更新LSTM网络来检测异常。在电机轴承真实故障数据集和PMSM定子故障数据集上的实验证明了该方法具有很强的电流特征融合和异常检测能力。