An unsupervised learning based simplification on ship motion model and its verification

Luo, Jiaqi; Shi, Ying; Xie, Lingyun

doi:10.1109/yac.2019.8787646

Cited by 1 publication

(1 citation statement)

References 6 publications

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“…Moreover, Abed et al [16] implemented a Neural Network for fault diagnosis of USV trolling motors trained on the data from the stator current and motor vibrations. Another interesting application of digital twins for USV is the system identification of hydrodynamic parameters using machine learning [17] and model-based methods [18]. In recent work, Kapteyn et al [19] proposed a mathematical framework for developing digital twins at scale.…”

Section: B Literature Reviewmentioning

confidence: 99%

Digital Twin-Driven Fault Diagnosis for Autonomous Surface Vehicles

2023

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This paper presents a digital twin-driven fault diagnosis approach based on a graphical model and an adaptive extended Kalman filter algorithm for autonomous surface vehicles. In contrast with the traditional adaptive Kalman filter algorithm, where the fault parameters are treated as extended state variables, the newly proposed adaptive extended Kalman filter algorithm estimates the magnitude of the faults by calculating the parameter estimation gains directly from the sensor systems. To this end, the algorithm utilizes data from a global navigation satellite system receiver assuming the persistence of excitation conditions on the control inputs. The algorithm is tested in the Otter, an autonomous surface vehicle developed by Maritime Robotics, in which one of its propellers is faulty. Technically, the digital twin receives real-time data from the sensor system, estimates the magnitude of the actuator faults, and visualizes the results in a web-based application using JavaScript with Three.js library and Mapbox for realworld 3D map generation. Information regarding the magnitude of the faults is important for fault-tolerant control. Simulation and experimental results show the proposed approach is able to detect and estimate the actuator faults accurately.INDEX TERMS Digital twin, fault diagnosis, autonomous systems.

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