Identification of key factors affecting the failure of aviation piston engine turbochargers based on an improved correspondence analysis-polar angle-based classification

Turbocharging is an efficient approach for addressing power reduction and oil consumption increase in aviation piston engines during high-altitude flights.However,a turbocharger significantly increases the complexity of a power system,and its considerably complex matching relation with the engine results in a coupling of failure modes.Conventional analytical methods are hard to identify failure-inducing factors.Consequently,safety issues are becoming increasingly prominent.This study focuses on methods for identifying failure-inducing factors.A whole-machine system model is established and validated through experimentation.The response surface method is employed to further abstract the system simulation model to a surrogate model(average error:~3%)in order to reduce the computational cost while ensuring accuracy.On this basis,an improved Correspondence Analysis(CA)-Polar Angle(PA)-based Classification(PAC)is proposed to identify the key factors affecting the failure mode of turbochargers.This identification method is based on the row profile coordinates G varying with the numerical deviations of the key factors,and is capable of effectively identifying the key factors affecting the failure.In a validation example,this method identifies the diameter of the exhaust valve(e_(2))as the primary factor affecting the safety margin for each work boundary.