Anomaly detection methods for unmanned underwater vehicle performance data

Abstract

This thesis considers the problem of detecting anomalies in performance data for unmanned underwater vehicles(UUVs). UUVs collect a tremendous amount of data, which operators are required to analyze between missions to determine if vehicle systems are functioning properly. Operators are typically under heavy time constraints when performing this data analysis. The goal of this research is to provide operators with a post-mission data analysis tool that automatically identifies anomalous features of performance data. Such anomalies are of interest because they are often the result of an abnormal condition that may prevent the vehicle from performing its programmed mission. In this thesis, we consider existing one-class classification anomaly detection techniques since labeled training data from the anomalous class is not readily available. Specifically, we focus on two anomaly detection techniques: (1) Kernel Density Estimation (KDE) Anomaly Detection and (2) Local Outlier Factor. Results are presented for selected UUV systems and data features, and initial findings provide insight into the effectiveness of these algorithms. Lastly, we explore ways to extend our KDE anomaly detection algorithm for various tasks, such as finding anomalies in discrete data and identifying anomalous trends in time-series data.