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Proceedings of the 20th Conference on Computer Science and Intelligence Systems (FedCSIS)

Annals of Computer Science and Information Systems, Volume 43

Anomaly Detection for Unmanned Surface Vehicles Based on a Multi-Modal Bayesian Generative Model

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DOI: http://dx.doi.org/10.15439/2025F3412

Citation: Proceedings of the 20th Conference on Computer Science and Intelligence Systems (FedCSIS), M. Bolanowski, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 43, pages 399–404 ()

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Abstract. In this paper, we propose a novel method for abnormality detection in Unmanned Surface Vehicles (USVs) based on a Multi-Modal Bayesian generative model to enhance safety and monitoring. During the training phase, we use a Null Force Filter and an unsupervised clustering algorithm on multimodal data collected from Global Positioning System (GPS) and motor current sensors. In the testing phase, we use a Coupled Modified Markov Jump Particle Filter (CM-MJPF) to infer the GPS position and motor current of the USV, as well as to detect abnormalities in both modalities. Due to the coupled methodology, the system is able to learn the statistical similarity between the evolving GPS and motor current data. As a result, the causality of defects is inherently captured within the dynamical inference, making the proposed approach explainable.

References

  1. S. Baccari, M. Hadded, H. Ghazzai, H. Touati and M. Elhadef, "Anomaly Detection in Connected and Autonomous Vehicles: A Survey, Analysis, and Research Challenges," in IEEE Access, vol. 12, pp. 19250-19276, 2024, https://doi.org/10.1109/ACCESS.2024.3361829.
  2. G. Slavic, M. Bracco, L. Marcenaro, D. M. Gómez, C. Regazzoni, and P. Zontone, "Joint Data-Driven Analysis of Visual-Odometric Anomaly Signals in Generative Ai-Based Agents," 2024 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW), Seoul, Korea, Republic of, 2024, pp. 264-268, https://dx.doi.org/10.1109/ICASSPW62465.2024.10626634.
  3. S. Memon, A. Krayani, P. Zontone, L. Marcenaro, D. M. Gomez, and C. Regazzoni, "Learning 3D LiDAR Perception Models for Self-Aware Autonomous Systems," 2024 27th International Conference on Information Fusion (FUSION), Venice, Italy, 2024, pp. 1-8, https://dx.doi.org/10.23919/FUSION59988.2024.10706364.
  4. https://www.bayesserver.com/docs/techniques/anomaly-detection/
  5. Ö. Gültekin, E. Cinar, K. Özkan, and A. Yazıcı, “Multisensory data fusion-based deep learning approach for fault diagnosis of an industrial autonomous transfer vehicle,” Expert Systems with Applications, vol. 200, p. 117055, Aug. 15, 2022, https://doi.org/10.1016/j.eswa.2022.117055.
  6. Q. Shi and H. Zhang, "Fault Diagnosis of an Autonomous Vehicle With an Improved SVM Algorithm Subject to Unbalanced Datasets," in IEEE Transactions on Industrial Electronics, vol. 68, no. 7, pp. 6248-6256, July 2021, https://doi.org/10.1109/TIE.2020.2994868.
  7. Pratama, P.S., Gulakari, A.V., Setiawan, Y.D. et al. Trajectory tracking and fault detection algorithm for automatic guided vehicle based on multiple positioning modules. Int. J. Control Autom. Syst. 14, 400–410 (2016), https://doi.org/10.1007/s12555-014-0294-y
  8. Abci, B., El Badaoui El Najjar, M., Cocquempot, V. et al. An informational approach for sensor and actuator fault diagnosis for autonomous mobile robots. J Intell Robot Syst 99, 387–406 (2020), https://doi.org/10.1007/s10846-019-01099-7
  9. P. Castaldi, S. Farsoni, M. Menghini and S. Simani, "Data–Driven Fault Detection and Isolation of the Actuators of an Autonomous Underwater Vehicle," 2021 5th International Conference on Control and Fault-Tolerant Systems (SysTol), Saint-Raphael, France, 2021, pp. 139-144, https://doi.org/10.1109/SysTol52990.2021.9595605.
  10. Changxin Zhang and etal (2022). A data-driven fault detection and diagnosis method via just-in-time learning for unmanned ground vehicles. Automatika, 64(2), 277–290, https://doi.org/10.1080/00051144.2022.2142924
  11. Chen, Z.; Liang, K.; Peng, T.; Wang, Y. Multi-Condition PMSM Fault Diagnosis Based on Convolutional Neural network Phase Tracker. Symmetry 2022, 14, 295, https://doi.org/10.3390/sym14020295
  12. Chen, X.; Qin, P.; Chen, Y.; Zhao, J.; Li, W.; Mao, Y.; Zhao, T. Inter-Turn Short Circuit Fault Diagnosis of PMSM. Electronics 2022, 11, 1576, https://doi.org/10.3390/electronics11101576
  13. Ayankoso, S., He, Y., Gu, F., Ball, A. (2024). Multisensory Data-Based Fault Diagnosis of Induction Motors Using 1D and 2D Convolutional Neural Networks. In: Ball, A.D., Ouyang, H., Sinha, J.K., Wang, Z. (eds) Proceedings of the UNIfied Conference of DAMAS, IncoME and TEPEN Conferences (UNIfied 2023). TEPEN IncoME-V DAMAS 2023 2023 2023. Mechanisms and Machine Science, vol 152. Springer, Cham, https://doi.org/10.1007/978-3-031-49421-5_92
  14. Shaoxuan Xia, Xiaofeng Zhou, Haibo Shi, Shuai Li, Chunhui Xu, A fault diagnosis method with multi-source data fusion based on hierarchical attention for AUV, Ocean Engineering, Volume 266, Part 1,2022,112595, ISSN 0029-8018, https://doi.org/10.1016/j.oceaneng.2022.112595.