摘要
针对移动机器人驱动故障诊断误报率与漏报率较高的问题,设计了一种基于深度学习的移动机器人驱动故障自动诊断方法。设计移动机器人故障自诊断设备实现移动机器人运行数据采集。使用Softmax分类器,完成移动机器人驱动运行数据预处理。使用处理后的数据,构建移动机器人驱动故障诊断模型。至此,完成基于深度学习的移动机器人驱动故障自动诊断方法设计部分。实验结果表明,该方法有效地解决了原有方法驱动故障诊断误报率与漏报率较高的问题,推动了机器人故障诊断技术的发展。
Aiming at the problem of high false alarm rate and missing alarm rate of mobile robot drive fault diagnosis,an automatic diagnosis method of mobile robot drive fault based on deep learning is designed.The fault self diagnosis equipment of mobile robot is designed to realize the data acquisition of mobile robot.Softmax classifier is used to preprocess the driving data of mobile robot.Using the processed data,the driving fault diagnosis model of mobile robot is constructed.So far,the design part of driving fault automatic diagnosis method for mobile robot based on deep learning is completed.The experimental results show that this method effectively solves the problem of high false alarm rate and missing alarm rate of the original method,and promotes the development of robot fault diagnosis technology.
作者
董晓红
黄卉
杨嘉鹏
DONG Xiaohong;HUANG Hui;YANG Jiapeng(Xinjiang Institute of Engineering,Wulumuqi 830023,China)
出处
《自动化与仪器仪表》
2021年第8期196-199,共4页
Automation & Instrumentation
基金
新疆维吾尔自治区高校科研计划自然科学项目青年项目(No.XJEDU2020Y043)。
关键词
深度学习
移动机器人
故障诊断
自动诊断
数据采集
deep learning
mobile robot
fault diagnosis
automatic diagnosis
data acquisition
