Vision Monitoring of Half Journal Bearings

Iman Abulwaheed, Sangarappillai Sivaloganathan, Khalifa Harib

Research output: Chapter in Book/Report/Conference proceedingConference contribution


This paper describes a Machine Vision system developed to continuously monitor the wear in a system made up of half journal bearings that can form part of a smart factory. The continuous monitoring facilitates the estimation of wear characteristics such as wear rate, and remaining life that is not possible in the traditional observation of Mean Time Between Failures. It was also found that the system is amenable to be part of the IoT network in a Smart Factory. A centrally loaded shaft supported by two half-journal bearings and driven at 28 rpm by an ordinary AC electric motor through a worm gear (reduction 50:1) is considered. A Logitech C920 camera was set to monitor a half-journal bearing. MATLAB Image Processing Toolbox was used to program the acquiring of images and the processing of the acquired images. Wear at specified time intervals were obtained and tabulated. The data in the Table were used to estimate and monitor wear characteristics. The system and the software developed can be used as part of a smart factory where journal bearings form part of it and the operation of the bearings can be made autonomous during their lifetime.

Original languageEnglish
Title of host publicationIntelligent Computing - Proceedings of the 2019 Computing Conference
EditorsKohei Arai, Rahul Bhatia, Supriya Kapoor
PublisherSpringer Verlag
Number of pages15
ISBN (Print)9783030228705
Publication statusPublished - 2019
EventComputing Conference, 2019 - London, United Kingdom
Duration: Jul 16 2019Jul 17 2019

Publication series

NameAdvances in Intelligent Systems and Computing
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365


ConferenceComputing Conference, 2019
Country/TerritoryUnited Kingdom


  • Image processing
  • Jackson structured programming
  • Journal bearings
  • Machine vision

ASJC Scopus subject areas

  • Control and Systems Engineering
  • General Computer Science


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