基于移动网络在机械振动无线传感器网络中的旋转机械的边缘计算和故障诊断
Yi Huang1,2, Shuang Liang1, Tingqiong Cui1
1School of Intelligent Manufacturing Engineering, Chongqing University of Arts and Sciences, Chongqing 402160, China.
Sensors (Basel, Switzerland)
|August 29, 2024
概括
一种新的故障检测方法使用在机械振动无线传感器网络 (MvWSNs) 上的mobileNet用于旋转机械. 这种边缘计算方法显著减少了数据传输,并使近乎实时的故障检测成为可能.
科学领域:
- 事物的工业互联网 (IIoT)
- 旋转机械诊断 机械诊断 机械诊断
- 边缘计算 边缘计算
背景情况:
- 机械振动无线传感器网络 (MvWSNs) 越来越多的数据在旋转机械监控中压缩了带宽.
- 增强的实时处理对于工业环境中不断升级的安全要求至关重要.
- 传统的经验式故障检测方法不足以满足当前的需求.
研究的目的:
- 提出一种有效的故障检测方法,用于MvWSNs内的旋转机械.
- 为了解决带宽限制和增强实时处理能力.
- 在资源受限的传感器节点上实施深度学习模型.
主要方法:
- 开发了一个使用轻量级深度学习模型,MobileNet.Net的故障检测系统.
- 在MvWSN传感器节点上集成训练模型到边缘计算平台 (STM32微控制器).
- 在传感器节点上直接执行数据采集,处理和分类.
主要成果:
- 实现了高精度:在DDS数据集上为0.99,在MvWSNs传感器节点上为0.98.
- 减少数据传输大小,仅为原始数据的0.1%.
- 显著减少处理时间到每节点135ms,相比于原始数据传输的1000ms.
结论:
- 拟议的边缘计算故障检测方法有效地减轻了MvWSNs中的通信压力.
- 该系统可实现旋转机械的近实时传感和故障检测.
- 在工业环境中展示了对时间敏感故障检测和控制的强大应用潜力.
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