通过分析网络遥测数据，自动区分有线宽带故障来源，提升维修效率。
TelApart: Differentiating Network Faults from Customer-Premise Faults in Cable Broadband Networks
- 基于无监督学习识别设备异常模式，聚类相似故障特征。
- 利用客户报修工单数据自动调优模型参数，无需人工干预。
- 适用于真实运维场景，解决数据缺失与错位问题，适合运营商部署。
有线宽带网络中常出现两类射频干扰：一类源于网络内部，另一类发生在用户端。准确区分两者对匹配维修人员至关重要，但当前行业缺乏公开的自动化诊断工具。本文提出TelApart系统，利用有线网络中主动运维（PNM）基础设施采集的遥测数据，有效区分故障类型。系统核心为无监督机器学习模型，可将具有相似异常模式的设备分组。通过运营商客户报修工单数据自动调节模型超参数，实现跨场景快速部署而无需人工调参。针对PNM系统中常见的缺失、重复和不同步数据问题，本研究提出有效处理方案。基于某有线运营商的真实数据验证，结果表明TelApart能准确识别不同类型的故障。
原文摘要 · Abstract (English)
Two types of radio frequency (RF) impairments frequently occur in a cable broadband network: impairments that occur inside a cable network and impairments occur at the edge of the broadband network, i.e., in a subscriber's premise. Differentiating these two types of faults is important, as different faults require different types of technical personnel to repair them. Presently, the cable industry lacks publicly available tools to automatically diagnose the type of fault. In this work, we present TelApart, a fault diagnosis system for cable broadband networks. TelApart uses telemetry data collected by the Proactive Network Maintenance (PNM) infrastructure in cable networks to effectively differentiate the type of fault. Integral to TelApart's design is an unsupervised machine learning model that groups cable devices sharing similar anomalous patterns together. We use metrics derived from an ISP's customer trouble tickets to programmatically tune the model's hyper-parameters so that an ISP can deploy TelApart in various conditions without hand-tuning its hyper-parameters. We also address the data challenge that the telemetry data collected by the PNM system contain numerous missing, duplicated, and unaligned data points. Using real-world data contributed by a cable ISP, we show that TelApart can effectively identify different types of faults.
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