A fault diagnostic approach based on PSO-HMM for underwater thrusters

Zhenzhong Chu; Zhenhao Gu; Zhiqiang Li; Yunsai Chen; Mingjun Zhang

doi:10.3934/mbe.2022589

A fault diagnostic approach based on PSO-HMM for underwater thrusters

Math Biosci Eng. 2022 Aug 29;19(12):12617-12631. doi: 10.3934/mbe.2022589.

Authors

Zhenzhong Chu^{1

2}, Zhenhao Gu¹, Zhiqiang Li¹, Yunsai Chen³, Mingjun Zhang⁴

Affiliations

¹ Logistics Engineering College, Shanghai Maritime University, Shanghai 201306, China.
² School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
³ College of Shipbuilding Engineering, Harbin Engineering University, Harbin 150001, China.
⁴ College of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin 150001, China.

PMID: 36654014
DOI: 10.3934/mbe.2022589

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.

Keywords: Hidden Markov Model; fault diagnosis; particle swarm optimization; thruster; underwater vehicle.

MeSH terms

Algorithms*