Hybrid fault diagnosis for telerobotics system

Fawaz, Khaled; Ould-Bouamama, Belkacem

doi:10.1016/j.mechatronics.2010.01.009

Cited by 9 publications

(4 citation statements)

References 14 publications

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“…It is a mobile robot with four independently controlled electromechanical wheels and two controlled steering systems. 28 The quarter part of the vehicle describes the dynamics of the electromechanical system used to drive the wheels. Three sensors are used: one for current and the two others for angular velocities used for the motor rotor and the wheel, respectively.…”

Section: Application Of the Developed Methodsmentioning

confidence: 99%

Optimized fault detection using bond graph in linear fractional transformation form

Ouziala

Touati

Berrezouane

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part I:

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This article deals with the optimal robust fault detection problem using the bond graph in its linear fractional transformation form. Generally, this form of the bond graph allows the generation of two perfectly separate analytical redundancy relations, that are used as residual and threshold. However, the uncertainty calculation method gives overestimated thresholds. This may, for instance, lead to undetectable faults. Therefore, enhancing the robustness of fault detection and isolation algorithms is of utmost importance in designing a bond graph–based fault detection system. The main idea of this article is to develop optimized thresholds to ensure an optimal detection, otherwise this article proposes a method to detect tiny magnitude faults concerning parameter’s uncertainties. This work considers the issue of optimal fault detection as an optimization problem of the gap between the residuals and its threshold. New uncertainty values will be calculated in a way that these estimated parameters ensure the desired optimized gap between residuals and thresholds. These estimated uncertainty values will be used to generate optimized adaptive thresholds. Through these thresholds, we increase the sensitivity of the residuals to tiny magnitude faults, and we ensure an optimal and early detection.

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Section: Application Of the Developed Methodsmentioning

confidence: 99%

Optimized fault detection using bond graph in linear fractional transformation form

Ouziala

Touati

Berrezouane

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part I:

View full text Add to dashboard Cite

show abstract

“…The dynamic model of the robotic system can be derived via either the Lagrangian or Newton-Euler methods [10,12,18,28]. Generally, a healthy manipulator arm is described by the following system of differential equations: M( ) V( , ) G( ) ( , , , t) , θ θ+ θ θ + θ +µ θ θ τ = τ…”

Section: The Manipulator Robot Modelmentioning

confidence: 99%

Sensor and Actuator Fault Diagnosis Based on Soft Computing Techniques

Khireddine

Chafaa

Slimane

et al. 2015

Journal of Intelligent Systems

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Computational intelligence techniques are being investigated as an extension of the traditional fault diagnosis methods. This article presents, for the first time, a scheme for fault detection and isolation (FDI) via artificial neural networks and fuzzy logic. It deals with the sensor fault of a three-link selective compliance assembly robot arm (SCARA) robot. A second scheme is proposed for fault detection and accommodation via analytical redundancy, and it deals with the sensor fault of a three-link SCARA robot. These proposed FDI approaches are implemented on Matlab/Simulink software and tested under several types of faults. The results show the importance of this process. Then, the sensor faults are detected and isolated successfully. Also, the actuator faults are detected and a fault tolerance strategy is used for reconfigurable control using a sliding-mode observer.

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“…During the last few decades, robots have been used with success in various domains ranging from manufacturing (Merzouki et al, 2010), space exploration (Ambrose et al, 2010), and surgery (McMahan et al, 2011) to mining (Bednarz et al, 2011) and military assistance (Wooden et al, 2010). Developing robotic software is difficult and time-consuming, especially when the same functionality must be developed separately for robots with different physical dimensions, hardware control protocols, mechanical configurations, or actuators and sensors.…”

Section: Introductionmentioning

confidence: 99%

A Cross-Platform Tactile Capabilities Interface for Humanoid Robots

Dahl

2016

Front. Robot. AI

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This article presents the core elements of a cross-platform tactile capabilities interface (TCI) for humanoid arms. The aim of the interface is to reduce the cost of developing humanoid robot capabilities by supporting reuse through cross-platform deployment. The article presents a comparative analysis of existing robot middleware frameworks as well as the technical details of the TCI framework that builds on the existing YARP platform. Currently, the TCI framework includes robot arm actuators with robot skin sensors. It presents such hardware in a platform-independent manner, making it possible to write robot control software that can be executed on different robots through the TCI frameworks. The TCI framework supports multiple humanoid platforms, and this article also presents a case study of a cross-platform implementation of a set of tactile protective withdrawal reflexes that have been realized on both the NAO and iCub humanoid robot platforms using the same high-level source code.

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Hybrid fault diagnosis for telerobotics system

Cited by 9 publications

References 14 publications

Optimized fault detection using bond graph in linear fractional transformation form

Optimized fault detection using bond graph in linear fractional transformation form

Sensor and Actuator Fault Diagnosis Based on Soft Computing Techniques

A Cross-Platform Tactile Capabilities Interface for Humanoid Robots

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