Enhancing Fault Detection in AUV-Integrated Navigation Systems: Analytical Models and Deep Learning Methods
In complex underwater environments, the stability of navigation for autonomous underwater vehicles (AUVs) is critical for mission success. To enhance the reliability of the AUV-integrated navigation system, fault detection technology was investigated. Initially, the causes and classifications of fau...
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MDPI AG
2025-06-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/13/7/1198 |
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| author | Huibao Yang Bangshuai Li Xiujing Gao Bo Xiao Hongwu Huang |
| author_facet | Huibao Yang Bangshuai Li Xiujing Gao Bo Xiao Hongwu Huang |
| author_sort | Huibao Yang |
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| description | In complex underwater environments, the stability of navigation for autonomous underwater vehicles (AUVs) is critical for mission success. To enhance the reliability of the AUV-integrated navigation system, fault detection technology was investigated. Initially, the causes and classifications of faults within the integrated navigation system were analyzed in detail, and these faults were categorized as either abrupt or gradual, based on variations in sensor output characteristics under fault conditions. To overcome the limitations of the residual chi-square method in detecting gradual faults, a cumulative residual detection approach with error coefficient amplification was proposed. The algorithm enhances gradual fault detection by using eigenvalue analysis and constructing fault-frequency-based error amplification coefficients with non-parametric techniques. Furthermore, to improve the detection of gradual faults, artificial intelligence-based fault detection methods were also explored. Specifically, the particle swarm optimization (PSO) algorithm was employed to optimize the hyperparameters of a long short-term memory (LSTM) neural network, leading to the development of a PSO-LSTM fault detection model. In this model, the fault detection function was formulated by comparing the predictions generated by the PSO-LSTM model with those derived from the Kalman filter. The experimental results demonstrated that the fault detection function formulated by PSO-LSTM exhibited enhanced sensitivity to gradual faults and enabled the timely isolation of faulty sensors. In unfamiliar marine regions, the PSO-LSTM method demonstrates greater stability and avoids the need to recalibrate detection thresholds for each sea area—an important advantage for AUV autonomous navigation in complex environments. |
| format | Article |
| id | doaj-art-56796d2f087e43db845e172e4fb82e8f |
| institution | DOAJ |
| issn | 2077-1312 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
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| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-56796d2f087e43db845e172e4fb82e8f2025-08-20T03:08:06ZengMDPI AGJournal of Marine Science and Engineering2077-13122025-06-01137119810.3390/jmse13071198Enhancing Fault Detection in AUV-Integrated Navigation Systems: Analytical Models and Deep Learning MethodsHuibao Yang0Bangshuai Li1Xiujing Gao2Bo Xiao3Hongwu Huang4School of Aerospace Engineering, Xiamen University, Xiamen 361000, ChinaSchool of Intelligent Connected Vehicle, Hubei University of Automotive Technology, Shiyan 442000, ChinaSmart Marine Science and Engineering, Fujian University of Technology, Fuzhou 350118, ChinaState Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle, Hunan University, Changsha 410008, ChinaSchool of Aerospace Engineering, Xiamen University, Xiamen 361000, ChinaIn complex underwater environments, the stability of navigation for autonomous underwater vehicles (AUVs) is critical for mission success. To enhance the reliability of the AUV-integrated navigation system, fault detection technology was investigated. Initially, the causes and classifications of faults within the integrated navigation system were analyzed in detail, and these faults were categorized as either abrupt or gradual, based on variations in sensor output characteristics under fault conditions. To overcome the limitations of the residual chi-square method in detecting gradual faults, a cumulative residual detection approach with error coefficient amplification was proposed. The algorithm enhances gradual fault detection by using eigenvalue analysis and constructing fault-frequency-based error amplification coefficients with non-parametric techniques. Furthermore, to improve the detection of gradual faults, artificial intelligence-based fault detection methods were also explored. Specifically, the particle swarm optimization (PSO) algorithm was employed to optimize the hyperparameters of a long short-term memory (LSTM) neural network, leading to the development of a PSO-LSTM fault detection model. In this model, the fault detection function was formulated by comparing the predictions generated by the PSO-LSTM model with those derived from the Kalman filter. The experimental results demonstrated that the fault detection function formulated by PSO-LSTM exhibited enhanced sensitivity to gradual faults and enabled the timely isolation of faulty sensors. In unfamiliar marine regions, the PSO-LSTM method demonstrates greater stability and avoids the need to recalibrate detection thresholds for each sea area—an important advantage for AUV autonomous navigation in complex environments.https://www.mdpi.com/2077-1312/13/7/1198autonomous underwater vehiclesnavigationfault detection technologycumulative residual detection methodlong short-term memory |
| spellingShingle | Huibao Yang Bangshuai Li Xiujing Gao Bo Xiao Hongwu Huang Enhancing Fault Detection in AUV-Integrated Navigation Systems: Analytical Models and Deep Learning Methods Journal of Marine Science and Engineering autonomous underwater vehicles navigation fault detection technology cumulative residual detection method long short-term memory |
| title | Enhancing Fault Detection in AUV-Integrated Navigation Systems: Analytical Models and Deep Learning Methods |
| title_full | Enhancing Fault Detection in AUV-Integrated Navigation Systems: Analytical Models and Deep Learning Methods |
| title_fullStr | Enhancing Fault Detection in AUV-Integrated Navigation Systems: Analytical Models and Deep Learning Methods |
| title_full_unstemmed | Enhancing Fault Detection in AUV-Integrated Navigation Systems: Analytical Models and Deep Learning Methods |
| title_short | Enhancing Fault Detection in AUV-Integrated Navigation Systems: Analytical Models and Deep Learning Methods |
| title_sort | enhancing fault detection in auv integrated navigation systems analytical models and deep learning methods |
| topic | autonomous underwater vehicles navigation fault detection technology cumulative residual detection method long short-term memory |
| url | https://www.mdpi.com/2077-1312/13/7/1198 |
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