基于机器学习的飞机动力装置运行可靠性

To study the operational reliability of aircraft power plants during flight missions, we analyze the time-varying law and related influencing factors of power plant operational reliability using the machine learning method, meanwhile considering the multi-dimensional and coupling characteristics influencing the operational reliability. An operational reliability analysis method is proposed for power plants considering three factors: the operating state of the power plant, the operating state of the aircraft, and the operating environment of the power plant. Based on the QAR (Quick Access Recorder) data of the actual operation of the aircraft, this method identifies three kinds of factors and 16 main characteristics related to the operational reliability analysis of the power plant. Combined with the space-time relationship of aircraft operation, non-parametric analysis of the working state characteristics and the performance margin of aircraft power plants is conducted using DEA (Data Envelopment Analysis). According to the proposed QAR data characteristics, the random forest and multivariable neural network regression algorithm is used to establish two kinds of operational reliability analysis models of power plants based on machine learning. Taking B737-800 aircraft as an example, this paper analyzes the power plant operation data of a flight mission from Beijing to Zhuhai, and studies the training and testing of two machine learning analysis models. The analysis results show that the features contributing most to the power plant operating state characteristics are calculated airspeed, flight time, and flight altitude; those to the power plant performance margin are power plant operating state characteristics, radar weather, and flight time. The two types of machine learning methods proposed can well reflect the time-varying reliability law of the power plant operation process, providing reference for power plant operation and special situation handling.