Improving EGT sensing data anomaly detection of aircraft auxiliary power unit

The reliability of the on-wing aircraft Auxiliary Power Unit (APU) decides the cost and the comfort of flight to a large degree. The most important function of APU is to help start main engines by providing compressed air. Especially on the condition of sudden shutdown in the air, APU can offer additional thrust for landing. Therefore, its condition monitoring has drawn much attention from the academic and industrial field. Among the on-wing sensing data which can reflect its condition, Exhaust Gas Temperature (EGT) is one of the most important parameters. To ensure the reliability of EGT, one kind of data-driven anomaly detection framework for EGT sensing data is proposed based on the Gaussian Process Regression and Kernel Principal Component Analysis. The situations of one-dimensional and two-dimensional input data for EGT anomaly detection are considered, respectively. The cross-validation experiments are carried out by utilizing the real condition data of APU, which are provided by China Southern Airlines Company Limited Shenyang Maintenance Base. The anomalous stuck condition of EGT sensing data is also detected. Experimental results show that the proposed EGT sensing data anomaly detection method can achieve better performance of false positive ratio, false negative ratio and accuracy.