Time-variant reliability analysis for industrial robot RV reducer under multiple failure modes using Kriging model

Qian, Huaming; Li, Yanfeng; Huang, Hong‐Zhong

doi:10.1016/j.ress.2020.106936

Cited by 54 publications

(21 citation statements)

References 51 publications

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“…Gearboxes are often mentioned as the component that is frequently responsible for a failure of rotary machines [18], including industrial robots [19]. The most critical components of a gearbox are the gears and bearings.…”

Section: The Concept Of Robot Wearmentioning

confidence: 99%

Method for Robot Manipulator Joint Wear Reduction by Finding the Optimal Robot Placement in a Robotic Cell

et al. 2021

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We describe a method for robotic cell optimization by changing the placement of the robot manipulator within the cell in applications with a fixed end-point trajectory. The goal is to reduce the overall robot joint wear and to prevent uneven joint wear when one or several joints are stressed more than the other joints. Joint wear is approximated by calculating the integral of the mechanical work of each joint during the whole trajectory, which depends on the joint angular velocity and torque. The method relies on using a dynamic simulation for the evaluation of the torques and velocities in robot joints for individual robot positions. Verification of the method was performed using CoppeliaSim and a laboratory robotic cell with the collaborative robot UR3. The results confirmed that, with proper robot base placement, the overall wear of the joints of a robotic arm could be reduced from 22% to 53% depending on the trajectory.

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Section: The Concept Of Robot Wearmentioning

confidence: 99%

Method for Robot Manipulator Joint Wear Reduction by Finding the Optimal Robot Placement in a Robotic Cell

et al. 2021

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“…All of this suggests that kinematic robot reliability is an essential study area with several applications. e kinematic reliability problem of robots can be divided into two categories: point reliability and time-dependent reliability [10][11][12][13]. e instantaneous failure rate at a point on the trajectory is reflected in point reliability regardless of whether motion is valid at other points, which commonly occurs during the measurement of the repeatability of positioning.…”

Section: Introductionmentioning

confidence: 99%

Kinematic Reliability Analysis of a 7-DOF Redundant Robot

Ding

et al. 2022

Journal of Robotics

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The kinematic reliability of robots, defined as the probability that the end-effector falls inside the specified safe boundary, is of great significance in predicting the accuracy achieved in reality. This work selects the 7 degrees-of-freedom (7-DOF) redundant robot as an example to conduct reliability analysis by utilizing the envelope method against time-related issues in this work. Since variables in industrial robots are very small relative to their means, the motion error functions are commonly linearized by the first-order Taylor’s formula to simplify calculation, and the failure models in all directions and attitude angles are then established through the probability method over the entire input interval. As a result, the actual accuracy of the robot in each pose component will be displayed, instead of merely considering the position error like other scholars. The principle of the proposed method is to transform a time-dependent problem into a time-independent one with the help of the failure extreme points and endpoints, so as to enhance the operation efficiency under the premise of ensuring accuracy. Finally, the simulation results verify that the relative error of the envelope method is less than 6.0% compared with that of the Monte Carlo simulation method, and the computational efficiency is higher than that of the Monte Carlo method, which demonstrates that the envelope method has better comprehensive performance.

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“…Lu et al [16] proposed a new method of rice disease recognition based on a deep convolution neural network and achieved good results. In [17], a timevarying reliability method of rotation vector (RV) reducer for industrial robots with multiple failure modes based on the Kriging model is proposed.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Rotation Vector Reducer for Industrial Robot Based on a Convolutional Neural Network

Yang¹,

Luo²,

Li³

2021

SV-JME

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As a key component of a mechanical drive system, the failure of the reducer will usually cause huge economic losses and even lead to serious casualties in extreme cases. To solve this problem, a two-dimensional convolutional neural network (2D-CNN) is proposed for the fault diagnosis of the rotation vector (RV) reducer installed on the industrial robot (IR). The proposed method can automatically extract the features from the data and reduce the connections between neurons and the parameters that need to be trained with its local receptive field, weight sharing, and subsampling features. Due to the aforementioned characteristics, the efficiency of network training is significantly improved, and verified by the experimental simulations. Comparative experiments with other mainstream methods are carried out to further validate the fault classification accuracy of the proposed method. The results indicate that the proposed method out-performs all the selected methods.

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Time-variant reliability analysis for industrial robot RV reducer under multiple failure modes using Kriging model

Cited by 54 publications

References 51 publications

Method for Robot Manipulator Joint Wear Reduction by Finding the Optimal Robot Placement in a Robotic Cell

Method for Robot Manipulator Joint Wear Reduction by Finding the Optimal Robot Placement in a Robotic Cell

Kinematic Reliability Analysis of a 7-DOF Redundant Robot

Fault Diagnosis of Rotation Vector Reducer for Industrial Robot Based on a Convolutional Neural Network

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