基于边缘计算和深度学习的光电探测设备故障检测

Fault detection of photoelectric detection equipment based on edge computing and deep learning

以提升光电探测设备故障检测效果为目标,提出基于边缘计算和深度学习的光电探测设备故障检测方法。首先利用多传感器采集光电探测设备工作状态信号,从信号提取特征,然后采用边缘计算技术搭建光电探测设备故障检测平台,并采用深度学习算法对光电探测设备故障检测样本进行建模,构建光电探测设备故障检测分类器,最后仿真测试结果表明,本方法能够高精度进行各种光电探测设备故障检测,光电探测设备故障检测正确率超过95%,光电探测设备故障检测时间控制在20 ms以内,获得了理想的光电探测设备故障检测结果。

Aiming at improving the fault detection effect of photoelectric detection equipment,a fault detection method of photoelectric detection equipment based on edge computing and deep learning is proposed.First,use multi-sensor to collect the working state signal of photoelectric detection equipment,extract features from the signal,and then use edge computing technology to build a photoelectric detection equipment fault detection platform,and use deep learning algorithm to model the photoelectric detection equipment fault detection samples,and build a photoelectric detection equipment fault detection classifier.Finally,simulation test results show that this method can detect various photoelectric detection equipment faults with high accuracy,The fault detection accuracy of photoelectric detection equipment is more than 95%,and the fault detection time of photoelectric detection equipment is controlled within 20 ms,so the ideal fault detection result of photoelectric detection equipment can be obtained.