方位(导航)
断层(地质)
状态监测
漏磁
加速度计
振动
计算机科学
节点(物理)
还原(数学)
实时计算
GSM演进的增强数据速率
故障检测与隔离
信号(编程语言)
工程类
电气工程
磁铁
人工智能
声学
地质学
物理
地震学
执行机构
操作系统
结构工程
程序设计语言
数学
几何学
作者
Xiaoxian Wang,Siliang Lu,Wenbing Huang,Qunjing Wang,Shiwu Zhang,Min Xia
出处
期刊:IEEE Transactions on Instrumentation and Measurement
[Institute of Electrical and Electronics Engineers]
日期:2021-01-01
卷期号:: 1-1
被引量:39
标识
DOI:10.1109/tim.2021.3051668
摘要
An efficient data reduction algorithm is designed and implemented on an industrial Internet of Things (IIoT) node for permanent magnet synchronous motor (PMSM) bearing fault diagnosis in variable speed conditions. Leakage flux and vibration signals are, respectively, acquired by a magnetic sensor and an accelerometer on the IIoT node in a noninvasive manner. These two signals are processed and mixed on the IIoT and transmitted to a server. The received signal is separated, the cumulative rotation angle is calculated, and the vibration signal is resampled for bearing fault identification. The proposed method can reduce about 95% of the transmission data while maintaining sufficient precision in bearing fault diagnosis in comparison to a traditional method. The proposed method based on edge computing reduces the power consumption, and hence it is suitable to use on a battery-supplied IIoT node for remote PMSM condition monitoring and fault diagnosis.
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