基于运行数据的航班运行关键风险因素推断  

Flight Operation Key Risk Factors Inference Based on Operation Data

作　　者：王岩韬[1] 李蕊[1] 卢飞[1] 唐建勋[1] 赵嶷飞[1] 

机构地区：[1]中国民航大学国家空管运行安全技术重点实验室,天津300300

出　　处：《交通运输系统工程与信息》2016年第1期182-188,216,共8页Journal of Transportation Systems Engineering and Information Technology

基　　金：民航局科技项目(20150204);中央高校基本科研业务费资助(3122014D041)~~

摘　　要：为降低由于运行控制人员个体差异导致的航班运行风险,提升航空公司运行控制能力,通过对航班运行程序的系统分析,结合运行数据,从飞行机组、机场、天气、航路和航空器等运行角度筛选风险因素,建立航班运行控制风险评估指标体系;利用基于事故树的贝叶斯网络分析方法,以历年不安全事件报告为样本,正向推理预测得到不安全事件发生概率;综合3种重要度分析结果,辨识关键致险因素.结果表明,机长和副驾驶技术水平、机组间搭配、与空管人员配合等5项关键风险因素概率值超过了40%,严重影响航班运行安全.风险推断结果与实际运行情况相符.In order to reduce the flight operation risk caused by flight operation controllers＇ individual differences, and further improve the airlines operation control ability, based on the systemic analysis of flight operation procedures, combined with the operation data, screening of risk factors from the aspects of flight crew, airports, weather, routes and aircraft, the flight operation control risk assessment index is established. Using the Bayesian network analysis method based on the fault tree and insecurity event analysis reports over the years as the sample data, the probability of occurrence of unsafe events by forward reasoning is predicted. Adopting three types of importance index analysis to identify the key risk factors. The result shows that five key risk factors probability value are more than 40% resulting in a high flight operation risk value, such as pilot and co-pilot＇s technical level, crew matching and air traffic controller cooperation, etc. Risk inference result is entirely consistent with actual operation situation.

关 键 词：航空运输 航班运行 风险推断 贝叶斯网络 重要度分析 

分 类 号：U8[交通运输工程] X949[环境科学与工程—安全科学]