A fault diagnostic approach based on PSO-HMM for underwater thrusters
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 classific...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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AIMS Press
2022-08-01
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| Series: | Mathematical Biosciences and Engineering |
| Subjects: | |
| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2022589?viewType=HTML |
| _version_ | 1828408456866955264 |
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| author | Zhenzhong Chu Zhenhao Gu Zhiqiang Li Yunsai Chen Mingjun Zhang |
| author_facet | Zhenzhong Chu Zhenhao Gu Zhiqiang Li Yunsai Chen Mingjun Zhang |
| author_sort | Zhenzhong Chu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-10T11:39:17Z |
| format | Article |
| id | doaj.art-39b0de9ab12d4720a75e3526b8bf6497 |
| institution | Directory Open Access Journal |
| issn | 1551-0018 |
| language | English |
| last_indexed | 2024-12-10T11:39:17Z |
| publishDate | 2022-08-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj.art-39b0de9ab12d4720a75e3526b8bf64972022-12-22T01:50:18ZengAIMS PressMathematical Biosciences and Engineering1551-00182022-08-011912126171263110.3934/mbe.2022589A fault diagnostic approach based on PSO-HMM for underwater thrustersZhenzhong Chu0Zhenhao Gu1Zhiqiang Li2Yunsai Chen3Mingjun Zhang41. Logistics Engineering College, Shanghai Maritime University, Shanghai 201306, China 2. School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China1. Logistics Engineering College, Shanghai Maritime University, Shanghai 201306, China1. Logistics Engineering College, Shanghai Maritime University, Shanghai 201306, China3. College of Shipbuilding Engineering, Harbin Engineering University, Harbin 150001, China4. College of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin 150001, ChinaIn 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.https://www.aimspress.com/article/doi/10.3934/mbe.2022589?viewType=HTMLunderwater vehiclethrusterfault diagnosishidden markov modelparticle swarm optimization |
| spellingShingle | Zhenzhong Chu Zhenhao Gu Zhiqiang Li Yunsai Chen Mingjun Zhang A fault diagnostic approach based on PSO-HMM for underwater thrusters Mathematical Biosciences and Engineering underwater vehicle thruster fault diagnosis hidden markov model particle swarm optimization |
| title | A fault diagnostic approach based on PSO-HMM for underwater thrusters |
| title_full | A fault diagnostic approach based on PSO-HMM for underwater thrusters |
| title_fullStr | A fault diagnostic approach based on PSO-HMM for underwater thrusters |
| title_full_unstemmed | A fault diagnostic approach based on PSO-HMM for underwater thrusters |
| title_short | A fault diagnostic approach based on PSO-HMM for underwater thrusters |
| title_sort | fault diagnostic approach based on pso hmm for underwater thrusters |
| topic | underwater vehicle thruster fault diagnosis hidden markov model particle swarm optimization |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2022589?viewType=HTML |
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