基于SPSO-SVR的融合航空发动机传感器故障诊断  

Research on sensor fault diagnosis of aero-engine based on data fusion of SPSO-SVR

作　　者：鲁峰[1] 黄金泉[1] 陈煜[1,2] 宋云峰[1] 

机构地区：[1]南京航空航天大学能源与动力学院,南京210016 [2]贵州黎阳航空发动机公司,贵阳561102

出　　处：《航空动力学报》2009年第8期1856-1865,共10页Journal of Aerospace Power

摘　　要：针对航空发动机常见的传感器故障问题,提出了一种利用改进的粒子群算法训练支持向量回归机,并利用融合机制将其应用于传感器故障诊断.论述了用一簇支持向量回归机(SVR)预测器对传感器进行实时检测,通过逻辑判断机制隔离故障传感器,并且依据剩余的无故障传感器信息实现信号重构.以某型航空发动机传感器在其整个工作范围内受到的冲击、偏置和漂移故障为例,验证了基于自协调粒子群优化支持向量回归机(SPSO-SVR)算法的融合诊断机制对传感器单一故障和多重故障具有较高的精度和计算效率.In consideration of the common sensor faults in aero-engine,a new algorithm was proposed based on support vector regression(SVR) trained by improved particle swarm optimization(PSO),and was used for sensor fault diagnosis system based on data fusion.A bank of SVR was applied to sensor fault detection,isolation and validation.This fault diagnosis system would isolate the fault sensor relying on the isolation mechanisms,and select the validation module for signal recovery when some fault sensors were detected...

关 键 词：航空发动机 传感器故障检测、隔离、重构 自协调粒子群优化 支持向量回归机 小波分析 

分 类 号：V263.6[航空宇航科学与技术—航空宇航制造工程]