A fault detection method for Auxiliary Power Unit based on monitoring parameters selection

Abstract

Auxiliary Power Unit (APU) is an indispensable component utilized in modern aircraft, which provides electrical and pneumatic power to the aircraft independently. What’s more, APU can help the main engines restart in case of main engine failure during flight. Thus there exists the need of APU monitoring. However, APU has not received sufficient attention in maintenance due to its relatively low cost and safety requirements compared to main engines. Line maintenance shows that APU is likely to fail in the start stage. Based on the Quick Access Recorder data supported by the airline, a fault detection method for APU is proposed in this paper. In order to improve the accuracy of fault detection, feature selection is carried out firstly to determine the optimal subset of monitoring parameters by Recursive Feature Elimination. Then fault detection is carried out by Support Vector Machine. Results show that Exhaust Gas Temperature is the most effective index among all monitoring parameters, and feature selection has a significant improvement on the accuracy of fault detection, which means higher accuracy with less monitoring cost. The method proposed in this paper also disposes that some monitoring parameters should be paid more attention in terms of the condition monitoring of APU.

Keywords

auxiliary power unit quick access recorder data feature selection fault detection recursive feature elimination support vector machine

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