Remote Monitoring of Production Operations using a Smart Gateway Device

使用智能网关设备远程监控生产运营

• 批准号: 560406-2020
• 负责人: Rahimi, Afshin
• 金额: $ 2.19万
• 依托单位: University of Windsor
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=707070
• 关键词: Remote; Monitoring; Production; Operations; using

In recent years, deep learning algorithms have surpassed human-level accuracy for many cognitive applications. This trend has motivated researchers to use deep learning algorithms to analyze collected data from sensors for many applications, such as health and autonomous driving. In the design of deep learning algorithms, the primary focus of optimization has been accuracy and throughput. However, by introducing deep learning in some applications, the massive amount of data should be analyzed, and real-time processing is critical; new techniques are needed to accelerate the process. Edge devices are especially promising for accelerating deep learning algorithms due to their low power budget and high efficiency. Using gateway devices to apply deep learning models is referred to as edge computing. The name edge indicates applying the model at one edge of a framework, which is different from cloud computing, applying the deep learning model on a remote server. Because of the low capability and reliability of the edge devices on data management and latency on cloud computing, fog architecture is introduced. This proposal aims to develop a computer vision and deep learning-based model to be implemented on a gateway device to accelerate the video inferencing and reliability. The proposed system is applied to the video recorded from manufacturing floors to obtain real-time inspection for process monitoring and proactive efficiency improvement. To achieve the best performance in gateway device and deep learning, fog computing is proposed to use the device and algorithms effectively.