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Special Issue "Selected Papers about Sensor Application from 16th International Conference on Automation Technology"

A special issue of Sensors (ISSN 1424-8220).

Deadline for manuscript submissions: 31 March 2020.

Special Issue Editors

Prof. Dr. Chien-Hung Liu
E-Mail Website
Guest Editor
Department of Mechanical Engineering, National Chung Hsing University, 250 Kuo Kuang Rd., Taichung 402, Taiwan
Fax: +886 4 2287 7170
Interests: high precision instrument design; laser engineering; smart sensors and actuators; optical device; optical measurement; metrology
Special Issues and Collections in MDPI journals
Prof. Dr. Chung-Hsien Kuo
E-Mail Website
Guest Editor
Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan
Interests: autonomous electric vehicle; autonomous mobile robot; robotic manipulator
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

The Special Issue will select extended papers about sensor application from the 16th International Conference on Automation Technology (Automation 2019). Automation 2019 is an annual conference of the Chinese Institute of Automation Engineers (CIAE). It provides an international platform for the smart automation research community to explore the state-of-the-art of sciences and technologies within academic and industrial applications. The extended papers will be sensor studies on topics related to artificial intelligence and machine learning, sensor networks, signal and image processing, machine vision, internet of things, automatic optical inspection, robotics, automatic measurement, intelligent manufacturing systems, robots and systems, opto-mechatronics, industry4.0, micro/nano systems, micro/precision/ultraprecision manufacturing systems, MEMS, nanotechnology and sensor networks, biotechnology and biomedical engineering, smart grids, and power systems and renewable energy.

http://automation2019.ntust.edu.tw/

Prof. Dr. Chien-Hung Liu
Prof. Dr. Chung-Hsien Kuo
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Sensors in smart automation
  • Sensors development in artificial intelligence
  • Sensors with robotics
  • Automatic measurement
  • Sensors in Industry 4.0
  • Sensors in mechatronics and opto-mechatronics
  • Sensors in micro/precision/ultraprecision manufacturing systems
  • Sensors in MEMS, nanotechnology, and sensor networks

Published Papers (1 paper)

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Research

Open AccessArticle
In Situ Diagnosis of Industrial Motors by Using Vision-Based Smart Sensing Technology
Sensors 2019, 19(24), 5340; https://doi.org/10.3390/s19245340 - 04 Dec 2019
Abstract
This study uses machine vision, feature extraction, and support vector machine (SVM) to compose a vibration monitoring system (VMS) for an in situ evaluation of the performance of industrial motors. The vision-based system respectively offers a spatial and temporal resolution of 1.4 µm [...] Read more.
This study uses machine vision, feature extraction, and support vector machine (SVM) to compose a vibration monitoring system (VMS) for an in situ evaluation of the performance of industrial motors. The vision-based system respectively offers a spatial and temporal resolution of 1.4 µm and 16.6 ms after the image calibration and the benchmark of a laser displacement sensor (LDS). The embedded program of machine vision has used zero-mean normalized correlation (ZNCC) and peak finding (PF) for tracking the registered characteristics on the object surface. The calibrated VMS provides time–displacement curves related to both horizontal and vertical directions, promising remote inspections of selected points without attaching additional markers or sensors. The experimental setup of the VMS is cost-effective and uncomplicated, supporting universal combinations between the imaging system and computational devices. The procedures of the proposed scheme are (1) setting up a digital camera, (2) calibrating the imaging system, (3) retrieving the data of image streaming, (4) executing the ZNCC criteria, and providing the time–displacement results of selected points. The experiment setup of the proposed VMS is straightforward and can cooperate with surveillances in industrial environments. The embedded program upgrades the functionality of the camera system from the events monitoring to remote measurement without the additional cost of attaching sensors on motors or targets. Edge nodes equipped with the image-tracking program serve as the physical layer and upload the extracted features to a cloud server via the wireless sensor network (WSN). The VMS can provide customized services under the architecture of the cyber–physical system (CPS), and this research offers an early warning alarm of the mechanical system before unexpected downtime. Based on the smart sensing technology, the in situ diagnosis of industrial motors given from the VMS enables preventative maintenance and contributes to the precision measurement of intelligent automation. Full article
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