A Step-wise Feature Selection Scheme for a Prognostics and Health Management System in Autonomous Ferry Crossing Operation

Abstract

Developing a reliable algorithm to detect faults automatically within critical components in autonomous ferries is essential for safe and cost-beneficial maritime operations. Autonomous ferries are equipped with hundreds of sensors. Thus, in order to support the algorithm, the input data should be subjected to a feature selection process. This paper introduces a novel step-wise feature selection scheme for prognostics and health management (PHM) system in autonomous ferries. The scheme mainly consists of two steps. The first step is the Pearson correlation analysis to reduce the redundant information among sensors. In order to study the importance of the selected features obtained by correlation analysis and removal of irrelevant features, the second step is sensitivity analysis (SA) based feature selection. The proposed scheme is evaluated on real-operational marine diesel engine data. In the experiments, both fault classification and fault detection demonstrate the feasibility of the proposed approach.