Electrostatic monitoring technology of particle charging information can facilitate online monitoring of aero-engine,which effectively enhances engine fault diagnosis and health managements.Unlike traditional engine state monitoring technologies,aircraft engine monitoring by gas path electrostatic monitoring not only covers the predicted information source itself,but also detects the information that can provide an early wings for initial fault states through gas path charging levels.This paper establishes a non-stationary time sequence change-point model for anomaly recognition of electrostatic signals based on change-point theory combined with difference meth-od of non-stationary time series.Finally,electrostatic induction data were utilized by the engine life test for a parti-cular aircraft to validate the proposed algorithm.The results indicate that the activity level and the event rate were 0.5—0.8(nc)and 50%,respectively,which were far greater than 4—12(pc)and 0—4% under normal working conditions of the engine.