摘要
为精准识别出电力巡检机器人网络的隐藏故障,设计了基于人工智能的电力巡检机器人网络故障自动化检测系统。获取电力巡检机器人网络海量数据,创建多维特征,并筛选出网络关键特征,以此类特征为输入,结合LSTM模型,构建Bi_LSTM模型,运用其长期依赖关系捕捉能力,在同时考虑网络历史数据与未来数据的前提下,实现对电力巡检机器人网络中隐藏故障的实时自动化检测。结果显示,该系统可依据所筛选的网络关键特征,实现各种网络隐藏故障的精准检测,为电力巡检机器人的稳定巡检提供保障。
To accurately identify hidden faults in the power inspection robot network,an artificial intelligence based automatic detection system for power inspection robot network faults was designed.Obtain massive data from the network of power inspection robots,create multidimensional features,and filter out key network features.Using these features as inputs and combining them with the LSTM model,construct the Bi_LSTM model.Utilize its ability to capture long-term dependencies,while considering both historical and future network data,achieve real-time automated detection of hidden faults in the network of power inspection robots.The results show that the system can accurately detect various hidden network faults based on the selected network key features,providing a guarantee for the stable inspection of power inspection robots.
作者
孙留存
于龙
刘斌
SUN Liucun;YU Long;LIU Bin(China Green Development Investment Group,Beijing 100020,China)
出处
《自动化与仪表》
2025年第2期63-65,72,共4页
Automation & Instrumentation
