External visual observers for understanding manufacturing operations & predicting faults

用于了解制造运营的外部视觉观察员

• 批准号: 577810-2022
• 负责人: Zelek, JohnJS
• 金额: $ 1.82万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=765903
• 关键词: External; visual; observers; understanding; manufacturing

ATS Automation Tooling Systems Inc. provide solutions for factory design and automation. Their life sciences division provides manufacturing solutions for world leading companies engaged in medical devices, pharmaceuticals & diagnostics. ATS also develops automation systems for the transportation, energy as wellsthe consumer & electronics sectors. The Vision Image Processing (VIP) lab at the University of Waterloo isdedicated to understanding visual processes and finding solutions for the outstanding problems in image andvideo processing and intelligent systems. ATS currently utilizes camera sensors in machine vision monitoringof existing processes in addition to a collection of other sensors.After a manufacturing machine is produced, it is difficult to retrofit an existing machine with new sensors. This project will explore external observing cameras that can be placed around the machine and learn what is the normal operation and be able to detect and predict anomalous operations. There are three machine scenarios of interest: (1) a conveyor system; (2) an assembly machine; and (3) a bowl feeder machine.however VIP hopes to improve robustness and accuracy by providing AI based algorithmsthat understand the process including the visual scene landscape of the manufacturing process components andparts.

ATS自动化工具系统公司为工厂设计和自动化提供解决方案。我们的生命科学部门为从事医疗设备、制药和诊断的世界领先公司提供制造解决方案。ATS还为运输、能源以及消费电子行业开发自动化系统。滑铁卢大学的视觉图像处理（VIP）实验室致力于了解视觉过程并为图像和视频处理以及智能系统中的突出问题寻找解决方案。ATS目前在现有工艺的机器视觉监控中使用摄像头传感器以及其他传感器的集合。在制造机器生产后，很难用新传感器改造现有机器。 该项目将探索可以放置在机器周围的外部观察摄像头，并了解什么是正常操作，并能够检测和预测异常操作。 有三种感兴趣的机器场景：（1）输送系统;（2）装配机;（3）碗式喂料机。然而，VIP希望通过提供基于AI的算法来提高鲁棒性和准确性，这些算法可以理解过程，包括制造过程组件和零件的视觉场景景观。