变转速工况下基于多传感器信号深度特征 融合的电机故障诊断研究

本文提出一种利用多传感器信号深度特征融合的方法实现电机变转速工况下的故障诊断。首先从多传感器节点同步采集电机的多通道振动、声音和漏磁信号。对漏磁信号进行处理获取电机转子的累积转角曲线，随后利用累积转角曲线对振动和声音信号进行阶比分析处理。最后利用双层双向长短期记忆网络从经过预处理的多传感器信号中提取和融合特征以诊断电机故障。实验结果表明，通过提取和融合8通道的电机振动和声音信号，本文提出的方法能够有效识别电机的高阻接触、偏心、霍尔断线、相间短路、轴承等10类运行状态，分类准确率达到99.86%。该方法有望部署在物联网边缘计算节点中，实现电机的远程在线状态监测和故障诊断。

Motor fault diagnosis based on deep feature fusion of multi-sensor data under variable speed condition

This article proposes a method based on the deep feature fusion of multi-sensor data for accurate motor fault diagnosis under varying speed condition. First, vibration, acoustic, and leakage magnetic signals are sampled from the data acquisition node. The accumulative rotating angle of the motor rotor is calculated from the leakage magnetic signal. Then, the order analysis is conducted on the vibration and acoustic signals based on the angle curve. Finally, the features of the pre-processed signals are extracted and fused by using the double-layer bidirectional long short-term memory (DBiLSTM) networks for fault pattern recognition. Experimental results show that the proposed method can identify 10 types of working conditions including high-resistance connection, eccentric, broken wire of the Hall sensor, interphase short circuit, and bearing faults with the accuracy of 99. 86% , by extracting and fusing of 8 channels of motor vibration and acoustic signals. The method is promising to be deployed into the internet of things edge computing node for remote online condition monitoring and fault diagnosis.