Anomaly Detection in PowerCells Auxiliary Power Unit

Abstract

In the paper of Hayton et.al [1], One-class Support Vector Machine is used for health monitoring of a jet engine in order to discover when and if an abnormal event has occured. Hayton et.al used the amplitude of the vibration data from the engine shaft as the feature data to the One-class Support Vector Machine algorithm. This approach works well when the sensor data is known to be periodic, with a certain frequency; however it can not be used if the sensor data has an irregular shape. In this paper we will extend the concept of Hayton et.al [1] and use the Discrete Wavelet Transform coefficients as input data to the OCSVM, rather than the Fourier Transform. This way we are able to classify more arbitrary sensor data found in PowerCells Auxilliary Power Unit (APU). We will also introduce a novel approach of how to select the hyperparameter s for the Radial Basis Function Kernel, in order to avoid both overfitting and underfitting.