Fault feature extraction and fusion method for AUV with weak thruster fault based on variational mode decomposition and D-S evidence theory
This study investigated the fault feature extraction and fusion problem for autonomous underwater vehicles with weak thruster faults. The conventional fault feature extraction and fusion method is effective when thruster faults are serious. However, for a weak thruster fault, that is, when the loss...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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AIMS Press
2022-06-01
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| Series: | Mathematical Biosciences and Engineering |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2022434?viewType=HTML |
| _version_ | 1828781888268926976 |
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| author | Dacheng Yu Mingjun Zhang Xing Liu Feng Yao |
| author_facet | Dacheng Yu Mingjun Zhang Xing Liu Feng Yao |
| author_sort | Dacheng Yu |
| collection | DOAJ |
| description | This study investigated the fault feature extraction and fusion problem for autonomous underwater vehicles with weak thruster faults. The conventional fault feature extraction and fusion method is effective when thruster faults are serious. However, for a weak thruster fault, that is, when the loss of effectiveness of thrusters is less than 10%, the following two problems occur if the conventional method is used. First, the ratio of fault features to noise features is small. Second, there is no monotonic relationship between the fusion fault features fused by the conventional method and the fault severity. In this paper, the following two methods are proposed to solve this problem: 1) Fault-feature extraction method. Based on negentropy, this method improves the evaluation index of the parameter optimization of the modified variational mode decomposition and finally enhances the fault features extracted by the modified Bayesian classification algorithm. 2) Fault-feature fusion method. To create a monotonic relationship between the fusion fault features and fault severity, this method expands the number of original signals of the traditional fusion method based on D-S evidence theory, improves the focus element of the traditional fusion method, and adopts the strategy of double fusion. Finally, the effectiveness of the proposed method was verified by pool-experiment results on Beaver II prototype. |
| first_indexed | 2024-12-11T17:40:06Z |
| format | Article |
| id | doaj.art-5774be838115477391e1309c10028d90 |
| institution | Directory Open Access Journal |
| issn | 1551-0018 |
| language | English |
| last_indexed | 2024-12-11T17:40:06Z |
| publishDate | 2022-06-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj.art-5774be838115477391e1309c10028d902022-12-22T00:56:33ZengAIMS PressMathematical Biosciences and Engineering1551-00182022-06-011999335935610.3934/mbe.2022434Fault feature extraction and fusion method for AUV with weak thruster fault based on variational mode decomposition and D-S evidence theoryDacheng Yu0Mingjun Zhang1Xing Liu2Feng Yao3College of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin, 150001, ChinaCollege of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin, 150001, ChinaCollege of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin, 150001, ChinaCollege of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin, 150001, ChinaThis study investigated the fault feature extraction and fusion problem for autonomous underwater vehicles with weak thruster faults. The conventional fault feature extraction and fusion method is effective when thruster faults are serious. However, for a weak thruster fault, that is, when the loss of effectiveness of thrusters is less than 10%, the following two problems occur if the conventional method is used. First, the ratio of fault features to noise features is small. Second, there is no monotonic relationship between the fusion fault features fused by the conventional method and the fault severity. In this paper, the following two methods are proposed to solve this problem: 1) Fault-feature extraction method. Based on negentropy, this method improves the evaluation index of the parameter optimization of the modified variational mode decomposition and finally enhances the fault features extracted by the modified Bayesian classification algorithm. 2) Fault-feature fusion method. To create a monotonic relationship between the fusion fault features and fault severity, this method expands the number of original signals of the traditional fusion method based on D-S evidence theory, improves the focus element of the traditional fusion method, and adopts the strategy of double fusion. Finally, the effectiveness of the proposed method was verified by pool-experiment results on Beaver II prototype.https://www.aimspress.com/article/doi/10.3934/mbe.2022434?viewType=HTMLautonomous underwater vehiclethrusterweak faultfault feature extractionfault feature fusion |
| spellingShingle | Dacheng Yu Mingjun Zhang Xing Liu Feng Yao Fault feature extraction and fusion method for AUV with weak thruster fault based on variational mode decomposition and D-S evidence theory Mathematical Biosciences and Engineering autonomous underwater vehicle thruster weak fault fault feature extraction fault feature fusion |
| title | Fault feature extraction and fusion method for AUV with weak thruster fault based on variational mode decomposition and D-S evidence theory |
| title_full | Fault feature extraction and fusion method for AUV with weak thruster fault based on variational mode decomposition and D-S evidence theory |
| title_fullStr | Fault feature extraction and fusion method for AUV with weak thruster fault based on variational mode decomposition and D-S evidence theory |
| title_full_unstemmed | Fault feature extraction and fusion method for AUV with weak thruster fault based on variational mode decomposition and D-S evidence theory |
| title_short | Fault feature extraction and fusion method for AUV with weak thruster fault based on variational mode decomposition and D-S evidence theory |
| title_sort | fault feature extraction and fusion method for auv with weak thruster fault based on variational mode decomposition and d s evidence theory |
| topic | autonomous underwater vehicle thruster weak fault fault feature extraction fault feature fusion |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2022434?viewType=HTML |
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