College of Civil Aviation, Nanjing University of Aeronautics & Astronautics，Nanjing, 211106, China
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摘要:
针对辅助动力装置(Auxiliary power unit,APU)故障预测时，仅基于快速存取记录器（Quick access recorder,QAR）数据存在实时性欠缺或精度不足的问题，提出了基于实时报文数据的APU故障预测方法。首先，对报文所采集的数据进行预处理，将每次航班的报文数据规整为一条数据集；其次，从参数阈值、维修记录及APU序列号变化情况等角度对数据集进行标注工作；随后，针对特征选择算法具有较差解释性的缺点，提出通过相关性分析选取能够表征APU运行性能的参数；最后，建立基于支持向量机（Support vector machine, SVM）的多参数故障预测模型并优化。经验证，该模型提高了预测正确率，为APU视情维修策略的制定提供参考。
Abstract:
Since in auxiliary power unit (APU) fault prediction quick access recorder(QAR) data cannot meet the requirement of real-time or accuracy， an APU fault prediction based on real-time message data is proposed. First， the data collected by the message is preprocessed， and the message data of each flight is organized and put into one data set. Second， the data set is marked from the perspectives of parameter threshold， maintenance record and changes of APU sequence number. Third， aiming at the poor interpretation of the feature selection algorithm， we select parameters that can characterize APU performance through correlation analysis. Finally， a multi-parameter fault prediction model based on support vector machine(SVM) is established and optimized. It is proved that this model can improve the prediction accuracy and provide reference for APU maintenance strategy.