Automatic fault diagnosis for autonomous underwater vehicles using online topic models

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

doi:10.1109/oceans.2016.7761139

Cited by 10 publications

(4 citation statements)

References 8 publications

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“…Recent studies on ground and marine robots proposed approaches to identify abnormal robot motion. 7,8 An anomaly or fault is defined as an unacceptable deviation of at least one characteristic property of a variable from an acceptable behavior. 9 We assume that normal behaviors occur far more frequently than abnormal behaviors for the data collected.…”

Section: Related Workmentioning

confidence: 99%

“…14 In 2015, a software configuration error in an underwater glider disabled an internal mass shifter that adjusts the vehicle's trim, leading the vehicle to sink to the seafloor and resulting in a temporary loss. 8 Instead of detecting faults in an individual component, robot motion can be used for anomaly detection. Raanan et al 15 use a threshold technique to prevent an underwater glider from hitting the seafloor.…”

Section: Related Workmentioning

confidence: 99%

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Learning and detecting abnormal speed of marine robots

Cho

Zhang

Edwards

2021

International Journal of Advanced Robotic Systems

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This article presents anomaly detection algorithms for marine robots based on their trajectories under the influence of unknown ocean flow. A learning algorithm identifies the flow field and estimates the through-water speed of a marine robot. By comparing the through-water speed with a nominal speed range, the algorithm is able to detect anomalies causing unusual speed changes. The identified ocean flow field is used to eliminate false alarms, where an abnormal trajectory may be caused by unexpected flow. The convergence of the algorithms is justified through the theory of adaptive control. The proposed strategy is robust to speed constraints and inaccurate flow modeling. Experimental results are collected on an indoor testbed formed by the Georgia Tech Miniature Autonomous Blimp and Georgia Tech Wind Measuring Robot, while simulation study is performed for ocean flow field. Data collected in both studies confirm the effectiveness of the algorithms in identifying the through-water speed and the detection of speed anomalies while avoiding false alarms.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Learning and detecting abnormal speed of marine robots

Cho

Zhang

Edwards

2021

International Journal of Advanced Robotic Systems

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“…In practice, most of the fault detection algorithms carried by AUVs are rule-based 2 . These rules are triggered when certain events occur, such as the device not responding, or a low battery alarm.…”

Section: Introductionmentioning

confidence: 99%

A fault detection method for AUV based on multi-scale spatiotemporal feature fusion

Xia

Zhou

Shi

et al. 2023

Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022)

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Autonomous underwater vehicles (AUVs) are important equipment for ocean development and exploration. To ensure the task implementation of AUV, faults shall be detected in time. We propose a fault detection method based on multiscale spatiotemporal feature fusion (MSF) for the time-varying characteristics and multiple correlation characteristics of AUV monitoring data. First, we apply a variety of sampling and data processing methods to generate monitoring windows with different scales along the time axis. Then, a composite feature extraction method is proposed to obtain temporal and spatial features simultaneously, and a feature pyramid of temporal and spatial information is formed. We use bidirectional long short-term memory (BiLSTM) to obtain the time-series characteristics of a single monitoring variable, and convolutional neural networks (CNN) to obtain the implicit spatial relationship characteristics among multiple monitoring variables. Next, we use an adaptive feature fusion method to solve the inconsistency in different feature scales, which can adaptively suppress the possible conflict information of different scale features. Finally, we use a fully connected network to detect the fault of the fused features. The fault detection experiment of Haizhe AUV shows the effectiveness and superiority of the proposed method.

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“…With the exploration of the ocean by human beings, a single AUV is no longer capable of handling the tasks with high operation volume and complexity, such as deep-sea resource exploration, underwater rescue, and deep-sea scientific research [1,2]. Therefore, the cooperative control of multiple AUVs has become a popular control problem [3].…”

Section: Introductionmentioning

confidence: 99%

Research on Formation Control Method of Heterogeneous AUV Group under Event-Triggered Mechanism

Chen¹,

Luo

Zhou

et al. 2022

Mathematics

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The time-sampling control strategy has communication discontinuities in the control of multiple AUVs (autonomous underwater vehicles). To overcome this problem, a distributed event-triggered communication mechanism is proposed to make each AUV communicate only when its own state is updated, which reduces the frequency of communication and improves the stability. This mechanism has better adaptability for formation control between heterogeneous AUV groups. At the same time, two consistency control algorithms based on event-triggered for homogeneous and heterogeneous AUV groups are studied, respectively. The known consistency algorithms are applied to the control of heterogeneous AUV groups for comparative analysis. The simulation results demonstrate that the number of communication among AUVs under the event-triggered control strategy can be significantly reduced. Therefore, the stability of the system is improved. Compared with the traditional consensus algorithm, the algorithm proposed in this paper has advantages in the control of heterogeneous AUV groups.

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Automatic fault diagnosis for autonomous underwater vehicles using online topic models

Cited by 10 publications

References 8 publications

Learning and detecting abnormal speed of marine robots

Learning and detecting abnormal speed of marine robots

A fault detection method for AUV based on multi-scale spatiotemporal feature fusion

Research on Formation Control Method of Heterogeneous AUV Group under Event-Triggered Mechanism

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