Robot Manipulator Drive Fault Diagnostics Using Data-Driven and Analytical Modelling
- Michael Lipsett
- Anthony Maltais
- Mohammad Riazi
- Nicolas Olmedo
- Osmar R. Zaiane, University of Alberta (Database)
Belt-driven mechatronic systems are popular for a range of applications. A modified robotic manipulator was adapted to allow different belt-drive faults to be incorporated into the mechanism, with additional sensors to characterize the compromised kinematics. Different data-driven models were used studied to detect anomalies in motor power consumption and end-effector motion; and a physics-based, lumped-parameter dynamic model was used to identify different faults. Comparative assessment metrics were sdused to compare the performance of different fault models from sets of laboratory test data.
Citation
M. Lipsett, A. Maltais, M. Riazi, N. Olmedo, O. Zaiane. "Robot Manipulator Drive Fault Diagnostics Using Data-Driven and Analytical Modelling". Machinery Failure Prevention Techology (MFPT), pp 11-22, May 2019.| Keywords: | Machinery diagnostics, fault detection, machine learning, modelling, robotics, analytics, belt drives, time-varying systems |
| Category: | In Conference |
BibTeX
@incollection{Lipsett+al:MFPT19,
author = {Michael Lipsett and Anthony Maltais and Mohammad Riazi and Nicolas
Olmedo and Osmar R. Zaiane},
title = {Robot Manipulator Drive Fault Diagnostics Using Data-Driven and
Analytical Modelling},
Pages = {11-22},
booktitle = {Machinery Failure Prevention Techology (MFPT)},
year = 2019,
}Last Updated: September 15, 2020Submitted by Sabina P
