Abnormality Detection Methods for Airborne Equipment's Working Performance Based on χ2 Distribution Model

In the research of Prognostics and Health Management aiming at the airborne equipments such as aeroengines, the working state of equipments can be efficiently monitored based on the flight data acquired, recorded and transported to the ground database by the Aircraft Condition Monitoring System. Firstly, the conception of normal working performance model and the Polynomial Coefficient Auto-Regressive model are introduced in the paper to help identify the abnormality of equipments. Secondly, based on chi-square distribution model, the abnormality detection algorithm based on chi-square test of standardized error sum of squares and the abnormality detection algorithm based on chi-square test of distribution fitting are put forward to detect the equipments' latent damage or fault. Compared to the former, the later can effectively reduce the rate of false alarm, however response unpunctually to the equipment's abnormality. Finally, the validity of algorithms is confirmed by the results of simulations aiming at a low pressure compressor rotor vibration amplitude sequence. It is indicated that the algorithms will be good tools for condition-based maintenance and autonomic logistics in future.