A Fault Detection and Health Monitoring Scheme for Ship Propulsion Systems Using SVM Technique

Abstract

Both the model-based and data-driven techniques for fault detection have their merits and drawbacks. The fault detection systems are usually laid out separately with the health monitoring systems in practice. In this paper, the well-established observer-based residual generator is formulated to construct multiple evaluation functions which are employed as the classification features of the support vector machine (SVM) for fault detection. It can be regarded as a tentative approach to combine the model-based and data-driven methods to enhance the fault detection performance. The standard SVM is modified for fault detection to achieve the quantitative tradeoff between false alarm rate and fault detection rate. In Addition, this paper also provides a unified framework for fault detection and health monitoring based on the SVM. Simulations on the ship propulsion system show the effectiveness of the proposed method.