贝叶斯网络支持下的计算机硬件系统故障诊断分析

Fault diagnosis analysis of computer hardware system supported by Bayesian network

文章首先分析了硬件故障的特性,提出了将概率图模型作为解决方案的思路,接着详细介绍了如何构建反映系统拓扑和组件间依赖关系的贝叶斯网络模型,最终实施了包括数据采集、学习和预测在内的完整流程。服务器集群实际验证表明,该模型能够以高达89%的准确率进行故障诊断,且预测时间最多可提前1.5 h,显示出明显优于传统规则方法的智能化和动态化特点。

This article first analyzes the characteristics of hardware failures and proposes the idea of using probability graph models as a solution.Then,it elaborates on how to build a Bayesian network model that reflects the system topology and dependency relationships between components.Finally,a complete process including data collection,learning,and prediction is implemented.The actual validation of server clusters shows that the model can diagnose faults with an accuracy of up to 89%,and the prediction time can be advanced by up to 1.5 hours,demonstrating significantly better intelligent and dynamic characteristics than traditional rule methods.