ABSTRACT
In this paper, we describe an approach based on improved Hidden Markov Model (HMM) for fault diagnosis of underwater thrusters in complex marine environments. First, considering the characteristics of thruster data, we design a three-step data preprocessing method. Then, we propose a fault classification method based on HMMs trained by Particle Swarm Optimization (PSO) for better performance than methods based on vanilla HMMs. Lastly, we verify the effectiveness of the proposed approach using thruster samples collected from a fault emulation experimental platform. The experiments show that the PSO-based training method for HMM improves the accuracy of thruster fault diagnosis by 17.5% compared with vanilla HMMs, proving the effectiveness of the method.
