基于健康画像的光通信设备故障预测算法

Fault prediction algorithm of optical communication equipment based on health profiles

针对光通信网设备风险预警无法进行人工分析的问题,提出基于用户画像技术与深度学习算法的设备故障预测算法。基于数据采集与数据增强构建设备健康画像,从含有脏数据且格式不统一的原始数据中抽取与设备故障相关联的标签序列,将序列数据“喂”入深度学习模型,获得高准确度的故障预测结果。仿真结果表明:该算法能够基于设备原始数据构建设备健康画像并实时训练深度学习模型,获得接近100%的设备故障预测准确率;与全标签序列的算法和未进行数据增强的算法相比,所提算法将故障预测准确率分别提升了7.8%和3.3%。

Aiming at the problem that the risk warning of optical communication network equipment cannot be analyzed manually,a failure prediction algorithm of equipment based on user profile and deep learning algorithm is proposed.Based on data collection and data enhancement,the device health profile is constructed,and the label sequence associated with the equipment failure is extracted from the original data containing dirty data and inconsistent format,and the sequence data is fed into the deep learning model to obtain highly accurate failure prediction results.The simulation results show that this algorithm can construct the equipment health profile based on the original equipment data and train the deep learning model in real time,and obtain the accuracy rate of equipment failure prediction close to 100%.Compared with the algorithm of full label sequence and the algorithm without data enhancement,the proposed algorithm improves the accuracy rate of failure prediction by 7.8%and 3.3%.