Load torque estimation for cable failure detection in cable-driven parallel robots: a machine learning approach

IF 2.6 2区 工程技术 Q2 MECHANICS
Jason Bettega, Giulio Piva, Dario Richiedei, Alberto Trevisani
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Abstract

This paper proposes a method for cable failure detection in cable-driven parallel robots (CDPRs) with arbitrary architecture, which is based on the estimates of the motor load torques, together with machine learning algorithms. By just exploiting the dynamic model of each actuator in the conditions of no load, an open-loop load torque observer is designed for each motor to estimate the presence of a load coupled through a cable. Since such a load instantaneously goes to zero for the motor with a broken cable, a simple but effective and robust signature of failure can be inferred to provide reliable detection even in the case of various model mismatches. Additionally, the load torque observer is not computationally demanding since just motor measurements are required, thus avoiding any direct measurement (and a dynamic model as well) on the end-effector. The detection of a failure is made through supervised classification algorithms based on artificial intelligence. The training of the machine learning algorithm is based on a “hybrid” approach: the dataset includes several failure cases, which are numerically generated through a system digital twin developed through the multibody system theory, together with measurements of the real system in nonfailing conditions. Different classification algorithms are considered, together with different sets of input variables to be fed to the classifier. Four numerical examples are proposed by showing the method capability in handling both fully actuated and redundantly actuated CDPRs under cable failure, both rigid and flexible cables, and also evaluating the response in the presence of cable slackness.

Abstract Image

用于缆索驱动并联机器人缆索故障检测的负载扭矩估算:一种机器学习方法
本文提出了一种在任意结构的缆索驱动并联机器人(CDPR)中进行缆索故障检测的方法,该方法基于对电机负载扭矩的估计,并结合机器学习算法。只需利用每个执行器在无负载条件下的动态模型,就能为每个电机设计一个开环负载扭矩观测器,以估计是否存在通过电缆耦合的负载。由于这种负载会在电缆断裂的电机上瞬间归零,因此即使在各种模型不匹配的情况下,也能推断出简单而有效、稳健的故障特征,从而提供可靠的检测。此外,负载扭矩观测器对计算要求不高,因为只需要对电机进行测量,从而避免了对末端执行器进行任何直接测量(以及动态模型)。故障检测是通过基于人工智能的监督分类算法进行的。机器学习算法的训练基于一种 "混合 "方法:数据集包括若干故障案例,这些案例是通过多体系统理论开发的系统数字孪生模型以及非故障条件下的真实系统测量结果数值生成的。考虑了不同的分类算法,以及输入分类器的不同输入变量集。通过展示该方法在处理电缆失效情况下的全驱动和冗余驱动 CDPR(刚性和柔性电缆)时的能力,以及评估电缆松弛情况下的响应,提出了四个数值示例。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
6.00
自引率
17.60%
发文量
46
审稿时长
12 months
期刊介绍: The journal Multibody System Dynamics treats theoretical and computational methods in rigid and flexible multibody systems, their application, and the experimental procedures used to validate the theoretical foundations. The research reported addresses computational and experimental aspects and their application to classical and emerging fields in science and technology. Both development and application aspects of multibody dynamics are relevant, in particular in the fields of control, optimization, real-time simulation, parallel computation, workspace and path planning, reliability, and durability. The journal also publishes articles covering application fields such as vehicle dynamics, aerospace technology, robotics and mechatronics, machine dynamics, crashworthiness, biomechanics, artificial intelligence, and system identification if they involve or contribute to the field of Multibody System Dynamics.
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