飞机空调系统的智能健康评价与退化预警: 一种融合任务剖面与 PCA-MD 的方法

Aiming at the problems of incomplete parameter characterization, imperfect feature extraction and inaccurate health evaluation caused by the weak correlation of physical information, mission process and environmental stress in the health state evaluation of aircraft air-conditioning system, this paper proposes an intelligent health evaluation and degradation warning method fusing mission profile and Principal Component Analysis- Mahalanobis Distance (PCA-MD) to provide a set of general solutions for intelligent health evaluation of complex systems of aviation equipment. First, this paper analyses the functional structure and typical failures of the air conditioning system, constructs a health characterization parameter system that includes the total level and the system level, and captures the coupled failure characteristics of the systems. Second, the impact of the flight mission on the air conditioning system is analyzed to design a health parameter spectrum based on the mission profile, which fully expresses the differences of the health parameters in the different mission phases. Third, a health baseline model based on Euclidean paradigm is constructed, which can comprehensively consider the influence of maintenance and inspection, as well as the environmental changes on the health baseline. Fourth, a health evaluation and degradation early warning method based on PCA-MD is proposed, which resolves the problem of the irreversibility of the covariance matrix in the construction of the health indexes and the inconsistency of the dimensions of the matrix based on the spectrum of the health parameters through the introduction of PCA, so as to achieve the efficient construction of health indexes, accurate evaluation of health state and early warning identification of performance degradation. Finally, this paper carries out an application validation study on the 1,474 practical data samples of the air conditioning system of a A320 airplane. The comparative results have validated the feasibility, practicability, and superiority of the proposed method.