摘要
针对航空发动机工作参数时变特性导致虚警率高的问题,采用自适应策略改进核主元分析模型进行异常监测。给出核主元分析异常监测框架,先用标准化的正常数据离线建模,后对实时数据计算统计量进行在线监测;为适应监测数据的时变特性,采用移动窗口自适应模型改进框架,利用实时采集的正常数据不断更新模型和监测数据。以航空发动机异常监测仿真实验进行验证。仿真结果表明:该方法能有效适应工作参数的变化,具有较高的故障检测率和较低的虚警率。
Aiming at the high wrong alarming rate caused by aero-engine work parameter time-varying feature, use self-adaptive strategy to improve kernel principal component analysis model for anomaly monitoring. The framework of anomaly monitoring based on kernel principal component analysis（KPCA） was presented. At first, use standardized normal data to establish off-line model, then carry on on-line monitoring for real time calculation statics. Because of time-varying feature of monitoring data, use moving window self-adaptive model to improve framework, and adopt real time normal data to renew model and monitoring data. Validate it by aero-engine anomaly monitoring simulation. Results of aero-engine simulation indicate that the proposed method can meet requirements of work parameter change, has higher fault detection rate and lower false alarm rate.
出处
《兵工自动化》
2016年第8期1-4,8,共5页
Ordnance Industry Automation
