Decentralised fault tolerance and fault detection of modular and reconfigurable robots with joint torque sensing

Abdul, S.; Liu, Guangjun

doi:10.1109/robot.2008.4543749

Cited by 20 publications

(7 citation statements)

References 24 publications

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“…11, each domain were dealt specifically for self-reconfigurable robots in the literatures. The sensing aspects for self reconfigurable robots are dealt in [52], [91]- [94] with fault detection discussed in [95]. The comprehensive work dealing with the planning in self reconfigurable robots is reported in [96].…”

Section: A Evaluation Method-i Of Autonomy Reconfigurabilitymentioning

confidence: 99%

A Framework for Taxonomy and Evaluation of Self-Reconfigurable Robotic Systems

et al. 2020

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Self-reconfigurable robots have been proposed for a quite long period and in large numbers. However, there are very few systematic methodologies proposed to categorize and evaluate such kinds of robots. In this paper, we put forward a framework for taxonomy and evaluation (TAEV) of self-reconfigurable robots, based on the mechanism reconfigurability and the level of autonomy for reconfiguration. The mechanism reconfigurability of the robots is divided into two types: inter-reconfigurability and intra-reconfigurability which are quantified by the number of configurations and scale respectively. A combination of both the intra-and inter-reconfigurability feature is named as nested-reconfiguration. The levels of autonomy reconfigurability are ranging through different levels from manual teleoperation to fully autonomous systems. The evaluation metrics are introduced to quantify the level of autonomy and the sufficiency of self-reconfigurable robots. Detailed discussions on applications of the proposed framework are presented with real-robot examples. INDEX TERMS Autonomy, autonomous systems, human-robot interaction, reconfigurability, reconfigurable robotics. A. MOTIVATION Although reconfigurability is useful and key for realizing adaptive and robust robotic systems, still the explicit

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Section: A Evaluation Method-i Of Autonomy Reconfigurabilitymentioning

confidence: 99%

A Framework for Taxonomy and Evaluation of Self-Reconfigurable Robotic Systems

et al. 2020

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“…During the last decades, there have been many published works in the subject area trying to address the fault detection and diagnosis issues in robot applications. To perform the robot actuator fault detection, approaches based on joint torque sensing have been reported [23,24]. In " [25], joint torque sensor signal is analyzed to determine the health of a robot.…”

Section: Health Monitoring and Fault Detectionmentioning

confidence: 99%

Power efficiency estimation based health monitoring and fault detection of modular and reconfigurable robot

Yaun¹

2021

Preprint

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Power efficiency degradation of machines often provides intrinsic indication of problems associated with their operation conditions. Inspired by this observation, in this thesis work, a simple yet effective power efficiency estimation base health monitoring and fault detection technique is proposed for modular and reconfigurable robot with joint torque sensor. The design of the Ryerson modular and reconfigurable robot system is first introduced, which aims to achieve modularity and compactness of the robot modules. Critical components, such as the joint motor, motor driver, harmonic drive, sensors, and joint brake, have been selected according to the requirement. Power efficiency coefficients of each joint module are obtained using sensor measurements and used directly for health monitoring and fault detection. The proposed method has been experimentally tested on the developed modular and reconfigurable robot with joint torque sensing and a distributed control system. Experimental results have demonstrated the effectiveness of the proposed method.

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“…However, the electric motor itself generates what is known as a back electromotive force (EMF) when subjected to mechanical load making them acting as a torque transducer (Yuan et al, 2011). The torque variations measurements via current fluctuations on robotic actuators have been applied for robot monitoring (Abdul andLiu, 2008, Yuan et al, 2011). The advantage of this technique to the robots health monitoring is that the motor current can remotely be measured along the power cables utilizing standard current sensors without supplementary instrumentation on the robot.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Industrial Robot Bearings Based on Discrete Wavelet Transform and Artificial Neural Network

Jaber

Bicker

2020

IJPHM

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Industrial robots have long been used in production systems in order to improve productivity, quality and safety in automated manufacturing processes. An unforeseen robot stoppage due to different reasons has the potential to cause an interruption in the entire production line, resulting in economic and production losses. The majority of the previous research on industrial robots health monitoring is focused on monitoring of a limited number of faults, such as backlash in gears, but does not diagnose the other gear and bearing faults. Thus, the main aim of this research is to develop an intelligent condition monitoring system to diagnose the most common faults that could be progressed in the bearings of industrial robot joints, such as inner/outer race bearing faults, using vibration signal analysis. For accurate fault diagnosis, time-frequency signal analysis based on the discrete wavelet transform (DWT) is adopted to extract the most salient features related to faults, and the artificial neural network (ANN) is used for faults classification. A data acquisition system based on National Instruments (NI) software and hardware was developed for robot vibration analysis and feature extraction. An experimental investigation was accomplished using the PUMA 560 robot. Firstly, vibration signals are captured from the robot when it is moving one joint cyclically. Then, by utilising the wavelet transform, signals are decomposed into multi-band frequency levels starting from higher to lower frequencies. For each of these levels the standard deviation feature is computed and used to design, train and test the proposed neural network. The developed system has showed high reliability in diagnosing several seeded faults in the robot.

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Decentralised fault tolerance and fault detection of modular and reconfigurable robots with joint torque sensing

Cited by 20 publications

References 24 publications

A Framework for Taxonomy and Evaluation of Self-Reconfigurable Robotic Systems

A Framework for Taxonomy and Evaluation of Self-Reconfigurable Robotic Systems

Power efficiency estimation based health monitoring and fault detection of modular and reconfigurable robot

Fault Diagnosis of Industrial Robot Bearings Based on Discrete Wavelet Transform and Artificial Neural Network

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