= PDF Reprint,     = BibTeX entry,     = Online Abstract

Click to download PDF version Click to download BibTeX data Clik to view abstract J. Windau, L. Itti, Inertial Machine Monitoring System for Automated Failure Detection, In: Proc. IEEE International Conference on Robotics and Automation (ICRA), pp. 93-98, May 2018. [2018 acceptance rate: 40.0%] (Cited by 8)

Abstract: Smart manufacturing technologies are emerging which combine industrial equipment with Internet-of-Things (IoT) sensors to monitor and improve productivity of manufacturing. This allows for new opportunities to explore algorithms for predicting machine failures from attached sensor data. This paper presents a solution to non-invasively upgrade an existing machine with an Inertial Machine Monitoring System (IMMS) to detect and classify equipment failure or degraded state. We also provide a strategy to optimize the amount, placement locations, and efficiency of the sensors. In experiments, the system collected data from 36 inertial sensors placed at multiple locations on a 3D printer. Normal operation vs. 10 types of realworld abnormal equipment behavior (loose belt, failures of machine components) were detected and classified by Support Vector Machines and Neural Networks. Using under 1 minute of recording while running a test print, a recursively discovered best subset of 4 to 9 sensors yielded 11-way classification accuracy over 99%. Our results suggest that even a small sensor network and short test program can yield effective detection of machine degraded state and can facilitate early remediation.

Themes: Beobots, Computer Vision


Copyright © 2000-2007 by the University of Southern California, iLab and Prof. Laurent Itti.
This page generated by bibTOhtml on Tue 10 Nov 2020 12:18:58 PM PST