Detection of unanticipated faults for autonomous underwater vehicles using online topic models

Raanan, Ben-Yair; Bellingham, James; Zhang, Yanwu; Kemp, Mathieu; Kieft, Brian; Singh, Hanumant; Girdhar, Yogesh

doi:10.1002/rob.21771

Cited by 29 publications

(10 citation statements)

References 37 publications

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“…(3) The model parameters for state-of-the-art fault detection methods remain constant and lack online adjustment. However, the model parameters can change due to complicated underwater conditions and long-term operations [6,[16][17][18], which may result in unpredicted faults [19].…”

Section: Introductionmentioning

confidence: 99%

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Online Learning Based Underwater Robotic Thruster Fault Detection

Guo

Zhao

et al. 2021

Applied Sciences

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This paper presents a novel online learning-based fault detection designed for underwater robotic thruster health monitoring. In the fault detection algorithm, we build a mathematical model between the control variable and the propeller speed by fitting collected online work status data to the model. To improve the accuracy of online modeling, a multi-center PSO algorithm with memory ability is utilized to optimize the modeling parameters. Additionally, a model online update mechanism is designed to accommodate the model to the change of thruster work status and sea environment. During the operation, propeller speed of the underwater robot is predicted through the online learning-based model, and the model residuals are used for thruster health monitoring. To avoid false alarm, an adaptive fault detection strategy is established based on model online update mechanism. The proposed method has been extensively evaluated using different underwater robotics, through a sea trial data simulation, a pool test fault detection experiment and a sea trial fault detection experiment. Compared with fixed model-based method, speed prediction MAE of the online learning model is at least 37.9% lower than that of the fixed model. The online learning-based method show no misdiagnosis in experiments, while the fixed model-based method is misdiagnosed. Experimental results show that the proposed method is competitive in terms of accuracy, adaptability, and robustness.

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Section: Introductionmentioning

confidence: 99%

“…A hidden Dirichlet distribution analysis model has been designed to detect the abnormality of the AUV vertical surface [19]. The AUV movement information and operation commands are coded into a string.…”

Section: Introductionmentioning

confidence: 99%

Online Learning Based Underwater Robotic Thruster Fault Detection

Guo

Zhao

et al. 2021

Applied Sciences

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“…The data-driven methods classify normal and faulty situations by identifying data patterns statistically, e.g., a novel data-driven algorithm was developed recently by integrating techniques of fast Fourier transform and uncorrelated multi-linear principal component analysis, which could achieve effective space visualization for FD under actuator and sensor faults [ 13 ]. Some other methods of implementations include recursive neural networks [ 14 ], online Bayesian nonparametric technique [ 15 ], wavelet-based filtering method [ 16 ], and energy-aware architecture [ 17 ]. Within these methods, the acquired data need to represent the fault types related to the investigated objects.…”

Section: Introductionmentioning

confidence: 99%

Active Fault Localization of Actuators on Torpedo-Shaped Autonomous Underwater Vehicles

Liu

Yan

Luo

et al. 2021

Sensors

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To ensure the mission implementation of Autonomous Underwater Vehicles (AUVs), faults occurring on actuators should be detected and located promptly; therefore, reliable control strategies and inputs can be effectively provided. In this paper, faults occurring on the propulsion and attitude control systems of a torpedo-shaped AUV are analyzed and located while fault features may induce confusions for conventional fault localization (FL). Selective features of defined fault parameters are assorted as necessary conditions against different faulty actuators and synthesized in a fault tree subsequently to state the sufficiency towards possible abnormal parts. By matching fault features with those of estimated fault parameters, suspected faulty sections are located. Thereafter, active FL strategies that analyze the related fault parameters after executing purposive actuator control are proposed to provide precise fault location. Moreover, the generality of the proposed methods is analyzed to support extensive implementations. Simulations based on finite element analysis against a torpedo-shaped AUV with actuator faults are carried out to illustrate the effectiveness of the proposed methods.

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“…Many efforts have been made to the fault detection of AUVs. In order to identify thruster failures, Fagogenis, et al [9] proposed an algorithm using a mixture of Gaussians and variational Bayes approximations in the diagnosis process, while [10] applied an online Bayesian nonparametric topic modeling technique to characterize the AUV's performance patterns automatically, then realized fault detection and diagnosis by means of a nearest-neighbor classifier. Reference [11] utilized a second-order sliding mode observer to estimate both the unmeasured system states and the fault extent related to the thrusters of a remotely operated vehicle.…”

Section: Introductionmentioning

confidence: 99%

A Fault-tolerant Steering Prototype for X-rudder Underwater Vehicles

Wang

Chen

Xia

et al. 2020

Sensors

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The X-rudder concept has been applied to more and more autonomous underwater vehicles (AUVs) in recent years, since it shows better maneuverability and robustness against rudder failure compared to the traditional cruciform rudder. Aiming at the fault-tolerant control of the X-rudder AUV (hereinafter abbreviated as xAUV), a fault-tolerant steering prototype system which can realize dynamics control, autonomous rudder fault detection and fault-tolerant control is presented in this paper. The steering prototype system is deployed on a verification platform, an xAUV, in which the monitor software is developed based on the factory method and the onboard software is developed based on the finite state machine (FSM). Dual-loop increment feedback control (DIFC) is first introduced to obtain smooth virtual rudder commands considering actuator’s limitations. Then the virtual rudder commands are transformed into X-rudder commands based on the mapping theory. In rudder fault diagnosis, an optimized particle filter is proposed for estimating rudder effect deduction, with proposal distribution derived from unscented Kalman filter (UKF). Then the fault type can be determined by analyzing indicators related to the deduction. Fault-tolerant control is addressed by dealing with nonlinear programming (NLP) problem, where minimization of allocation errors and control efforts are set as the optimization objectives, and rudder failure, saturation and actuators limitations are considered as constraints. The fixed-point iteration method is utilized to solve this optimization problem. Many field tests have been conducted in towing tank. The experimental results demonstrate that the proposed steering prototype system is able to detect rudder faults and is robust against rudder failure.

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Detection of unanticipated faults for autonomous underwater vehicles using online topic models

Cited by 29 publications

References 37 publications

Online Learning Based Underwater Robotic Thruster Fault Detection

Online Learning Based Underwater Robotic Thruster Fault Detection

Active Fault Localization of Actuators on Torpedo-Shaped Autonomous Underwater Vehicles

A Fault-tolerant Steering Prototype for X-rudder Underwater Vehicles

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