Spatio-Temporal Analysis and Prediction of Mass Telecommunication Base Station Failure Events

Large-scale telecommunication systems are lifeline infrastructure in modern society. A telecommunication system typically consists of a huge number of base stations with diverse geographical locations across a country, which highly complicates maintenance operations. To allocate maintenance resource properly, it is important to have a good understanding on the failure pattern of these base stations. Statistical inference of recurrent failures of these base stations is challenging because of the large number of base stations and the spatial correlation of their failure processes. Based on eight-month failure data of telecommunication base stations in Harbin, China, we propose a customized nonhomogeneous Poisson process (NHPP) model for recurrent failure data from telecommunication systems. The model consists of two layers, where the temporal layer applies an NHPP with station-specific frailty for failures of each base station, and the spatial layer uses a multivariate lognormal distribution to characterize the correlation among the frailties. The Monte Carlo EM (MCEM) algorithm is applied to estimate parameters included in the proposed model. We demonstrate the proposed model using the Harbin telecommunication system example with 7725 base stations and 4615 failure records.

大型电信系统是现代社会的生命线基础设施。电信系统通常由遍布全国、地理分布各异的海量基站构成，这极大提升了运维工作的复杂度。为合理配置运维资源，精准掌握这些基站的故障规律至关重要。由于基站数量庞大且故障过程存在空间相关性，针对基站复发性故障的统计推断颇具挑战。本研究基于中国哈尔滨地区电信基站为期8个月的故障观测数据，针对电信系统的复发性故障数据，提出了定制化非齐次泊松过程（nonhomogeneous Poisson process, NHPP）模型。该模型包含双层结构：时间层针对单个基站的故障，采用带有基站特异性脆弱因子的非齐次泊松过程；空间层则借助多元对数正态分布刻画脆弱因子之间的相关性。本研究采用蒙特卡洛EM（Monte Carlo EM, MCEM）算法对所提模型的参数进行估计。我们以包含7725个基站、4615条故障记录的哈尔滨电信系统为实例，对所提模型进行了验证演示。