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

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

doi:10.3934/mbe.2022589

Mathematical Biosciences and Engineering

2022, Volume 19, Issue 12: 12617-12631. doi: 10.3934/mbe.2022589

Previous Article Next Article

Research article Special Issues

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

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

Academic Editor: Hamid Reza Karimi

Received: 13 June 2022 Revised: 22 August 2022 Accepted: 22 August 2022 Published: 29 August 2022

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.
- underwater vehicle,
- thruster,
- fault diagnosis,
- Hidden Markov Model,
- particle swarm optimization
Citation: Zhenzhong Chu, Zhenhao Gu, Zhiqiang Li, Yunsai Chen, Mingjun Zhang. A fault diagnostic approach based on PSO-HMM for underwater thrusters[J]. Mathematical Biosciences and Engineering, 2022, 19(12): 12617-12631. doi: 10.3934/mbe.2022589

Related Papers:

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.

References

[1]	C. Knight, S. McGarry, J. Hayward, P. Osman, S. Behrens, A review of ocean energy converters, with an Australian focus, AIMS Energy, 2 (2014), 295–320. https://doi.org/10.3934/energy.2014.3.295 doi: 10.3934/energy.2014.3.295
[2]	C. Wu, Y. Dai, L. Shan, Z. Zhu, Z. Wu, Data-driven trajectory tracking control for autonomous underwater vehicle based on iterative extended state observer, Math. Biosci. Eng., 19 (2022), 3036–3055. https://doi.org/10.3934/mbe.2022140 doi: 10.3934/mbe.2022140
[3]	Z. Chu, F. Wang, T. Lei, C. Luo, Path planning based on deep reinforcement learning for autonomous underwater vehicles under ocean current disturbance, IEEE Trans. Intell. Veh., 2022 (2022). https://doi.org/10.1109/TIV.2022.3153352 doi: 10.1109/TIV.2022.3153352
[4]	X. Li, Y. Song, J. Guo, C. Feng, G. Li, T. Yan, et al., Sensor fault diagnosis of autonomous underwater vehicle based on extreme learning machine, in 2017 IEEE Underwater Technology (UT), (2017), 1–5. https://doi.org/10.1109/UT.2017.7890303
[5]	Y. Chen, Z. Chu, K. Liu, L. Yang, D. Zhu, Research progress on thruster fault diagnosis technology for deep-sea underwater vehicle, J. Propul. Technol., 41 (2020), 2465–2474. https://doi.org/10.13675/j.cnki.tjjs.200274 doi: 10.13675/j.cnki.tjjs.200274
[6]	S. Nascimento, M. Valdenegro-Toro, Modeling and soft-fault diagnosis of underwater thrusters with recurrent neural networks, IFAC-PapersOnLine, 51 (2018), 80–85. https://doi.org/10.1016/j.ifacol.2018.09.473 doi: 10.1016/j.ifacol.2018.09.473
[7]	Z. Chu, F. Meng, D. Zhu, C. Luo, Fault reconstruction using a terminal sliding mode observer for a class of second-order MIMO uncertain nonlinear systems, ISA Trans., 97 (2020), 67–75. https://doi.org/10.1016/j.isatra.2019.07.024 doi: 10.1016/j.isatra.2019.07.024
[8]	A. Shumsky, A. Zhirabok, C. Hajiyev, Observer based fault diagnosis in thrusters of autonomous underwater vehicle, in 2010 Conference on Control and Fault-Tolerant Systems (SysTol), (2010), 11–16. https://doi.org/10.1109/SYSTOL.2010.5676076
[9]	M. Kordestani, M. Saif, M. E. Orchard, R. Razavi-Far, K. Khorasani, Failure prognosis and applications—A survey of recent literature, IEEE Trans. Reliab., 70 (2021), 728–748. https://doi.org/10.1109/TR.2019.2930195 doi: 10.1109/TR.2019.2930195
[10]	K. Zhong, M. Han, B. Han, Data-driven based fault prognosis for industrial systems: a concise overview, IEEE/CAA J. Autom. Sin., 7 (2020), 330–345. https://doi.org/10.1109/JAS.2019.1911804 doi: 10.1109/JAS.2019.1911804
[11]	D. Zhu, X. Cheng, L. Yang, Y. Chen, S. X. Yang, Information fusion fault diagnosis method for deep-sea human occupied vehicle thruster based on deep belief network, IEEE Trans. Cybern., 52 (2022), 9414–9427. https://doi.org/10.1109/TCYB.2021.3055770 doi: 10.1109/TCYB.2021.3055770
[12]	Y. Wang, W. Zhang, F. Di, W. Gong, An AUV thruster fault diagnosis method based on the improved SVDD, in 2018 IEEE 8th International Conference on Underwater System Technology: Theory and Applications (USYS), (2018), 1–5. https://doi.org/10.1109/USYS.2018.8778887
[13]	Z. Chu, Z. Li, Z. Gu, Y. Chen, M. Zhang, A fault diagnosis method for underwater thruster based on RFR-SVM, Proc. Inst. Mech. Eng., Part M: J. Eng. Marit. Environ., 2022 (2022). https://doi.org/10.1177/14750902221095423 doi: 10.1177/14750902221095423
[14]	J. He, Y. Li, J. Cao, Y. Li, Y. Jiang, L. An, An improved particle filter propeller fault prediction method based on grey prediction for underwater vehicles, Trans. Inst. Meas. Control, 42 (2020), 1946–1959. https://doi.org/10.1177/0142331219901202 doi: 10.1177/0142331219901202
[15]	V. Filaretov, A. Zuev, A. Zhirabok, Development of fault detection and identification system for thrusters of underwater robots, in 2019 International Multi-Conference on Industrial Engineering and Modern Technologies (FarEastCon), (2019), 1–6. https://doi.org/10.1109/FarEastCon.2019.8934386
[16]	H. R. Karimi, Y. Lu, Guidance and control methodologies for marine vehicles: a survey, Control Eng. Pract., 111 (2021), 104785. https://doi.org/10.1016/j.conengprac.2021.104785 doi: 10.1016/j.conengprac.2021.104785
[17]	L. R. Rabiner, A tutorial on hidden Markov models and selected applications in speech recognition, Proc. IEEE, 77 (1989), 257–286. https://doi.org/10.1109/5.18626 doi: 10.1109/5.18626
[18]	Q. Xu, Z. Liu, H. Zhao, Method of turnout fault diagnosis based on hidden Markov model, J. China Railw. Soc., 40 (2018), 98–106. https://doi.org/10.3969/j.issn.1001-8360.2018.08.013 doi: 10.3969/j.issn.1001-8360.2018.08.013
[19]	M. Soleimani, F. Campean, D. Neagu, Integration of Hidden Markov Modelling and Bayesian Network for fault detection and prediction of complex engineered systems, Reliab. Eng. Syst. Saf., 215 (2021), 107808. https://doi.org/10.1016/j.ress.2021.107808 doi: 10.1016/j.ress.2021.107808
[20]	P. Arpaia, U. Cesaro, M. Chadli, H. Coppier, L. De Vito, A. Esposito, et al., Fault detection on fluid machinery using Hidden Markov Models, Measurement, 151 (2020), 107126. https://doi.org/10.1016/j.measurement.2019.107126 doi: 10.1016/j.measurement.2019.107126
[21]	B. A. Fernandes, G. D. Colletta, L. H. C. Ferreira, O. O. Dutra, Utilization of Savitzky-Golay filter for power line interference cancellation in an embedded electrocardiographic monitoring platform, in 2017 IEEE International Symposium on Medical Measurements and Applications (MeMeA), (2017), 227–232. https://doi.org/10.1109/MeMeA.2017.7985880
[22]	R. W. Schafer, What Is a Savitzky-Golay Filter? [Lecture Notes], IEEE Signal Process. Mag., 28 (2011), 111–117. https://doi.org/10.1109/MSP.2011.941097 doi: 10.1109/MSP.2011.941097
[23]	G. Xu, X. Wang, Y. Zhao, Adaptive fault diagnosis for thruster system of underwater vehicles, Ship Sci. Technol., 42 (2020), 95–100. https://doi.org/10.3404/j.issn.1672-7649.2020.06.019 doi: 10.3404/j.issn.1672-7649.2020.06.019
[24]	J. Kennedy, R. Eberhart, Particle swarm optimization, in Proceedings of ICNN'95 - International Conference on Neural Networks, 4 (1995), 1942–1948. https://doi.org/10.1109/ICNN.1995.488968

Reader Comments

Your name:*

Email:*
© 2022 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)