摘要
为有效分析光通信系统关键设备状态,保障光通信系统安全稳定运行,提出基于大数据分析的光通信系统关键设备状态识别方法。利用无线传感技术采集光通信系统关键设备状态信号,经小波阈值方法去噪后,利用数DL-GA算法检测异常数据信号,将异常数据信号上传至设备状态识别与管理中心,由设备状态诊断模块、设备寿命预测模块、设备识别管理模块,完成对设备状态、寿命的诊断、预测以及可视化管理。实验结果表明:该方法可有效识别光通信系统关键设备状态,信号识别准确性高于99%,识别时间低于187 ms,提高了数据去噪以及异常数据信号检测的效果。
In order to effectively analyze the status of key equipment of optical communication system and ensure the safe and stable operation of optical communication system,a method for identifying the status of key equipment of optical communication system based on big data analysis is proposed.Using wireless sensing technology to collect the key equipment status signals of the optical communication system,after denoising by the wavelet threshold method,the abnormal data signals are detected by the digital DL-GA algorithm,and the abnormal data signals are uploaded to the equipment status identification and management center.Module,equipment life prediction module,equipment identifi-cation management module,complete the diagnosis,prediction and visual management of equipment status and life.The experimental results show that the method can effectively identify the key equipment status of the optical communi-cation system,signal identification accuracy is higher than 99%,and the identification time is less than 187ms,which improves the effect of data denoising and abnormal data signal detection.
作者
周鹤
黄建军
ZHOU He;HUANG Jianjun(Nanchang Institute of Technology,Nanchang 330044,China)
出处
《激光杂志》
CAS
北大核心
2023年第12期167-172,共6页
Laser Journal
基金
江西省教育厅科学技术项目(No.GJJ212125)。
关键词
大数据分析
光通信系统
关键设备状态
状态识别
小波去噪
异常检测
big data analysis
optical communication system
key equipment status
status recognition
wavelet denoising
anomaly detection
