A study on the application of the aero-engine exhaust gas electrostatic monitoring technology for gas path component condition monitoring was conducted.The exhaust gas electrostatic monitoring signal (EGEMS) was taken as a new gas path parameter and its deviation from the baseline in EGEMS root mean square (RMS) value was monitored for real-time gas path component anomaly detection. Firstly

the exhaust gas soot emission characteristics were studied from the electrostatic monitoring point of view

and then the baseline composition of the EGEMS

the main influencing factors as well as the EGEMS features of the typical faults were studied. Furthermore

two baseline model mining methods were proposed respectively for a single parameter (fuel flow rate) based baseline model (parametric model) and a multi-parameter based baseline model (nonparametric model) which was obtained using multi-state estimation technique. The case study on a real EGEMS data set from a turbo-shaft engine shows that the established baseline model can characterize the EGEMS under various operating conditions of the test engine

and can effectively detect the anomaly of the gas path components.