光通信技术2025，Vol.49Issue(3)：27-33,7.DOI:10.13921/j.cnki.issn1002-5561.2025.03.005
基于深度交叉网络的多任务学习OLT设备故障识别算法
Fault identification algorithm for OLT equipment based on deep cross network and multi-task learning
摘要
Abstract
To address the issues of artificial intelligence(AI)model bias and insufficient feature learning caused by imbalanced optical network datasets,this paper proposes a fault identification algorithm for optical line terminal(OLT)equipment based on deep cross network(DCN)and multi-task learning(MTL).First,potential faults are assessed using standardized mean Manhattan distance,with high-similarity samples labeled as poor-quality data.Subsequently,a DCN-MTL model is constructed,incorporat-ing high-order feature interactions to enhance learning capability,while utilizing poor-quality detection as an auxiliary task to op-timize the training of the primary fault detection task.Experimental results demonstrate that,compared to traditional deep neural networks,the proposed algorithm achieves improvements of 1.15％in accuracy,11.83％in recall,6.39％in F1-score,and 5.91％in area under the curve(AUC)under the same data volume,with all metrics exceeding 0.95.This validates the algorithm's strong detection capability in scenarios with scarce fault data.关键词
光接入网/故障检测/数据集不均衡/深度交叉网络/多任务学习Key words
optical access networks/fault detection/dataset imbalance/deep cross network/multi-task learning分类
电子信息工程引用本文复制引用
毛仕龙,赵赞善,王皓宇,高冠军..基于深度交叉网络的多任务学习OLT设备故障识别算法[J].光通信技术,2025,49(3):27-33,7.基金项目
国家重点研发计划资助项目(2022YFB2903300)资助 （2022YFB2903300）
国家自然科学基金资助项目(62371064)资助. （62371064）
