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工厂环境的移动监测和嵌入式控制系统。

Mobile monitoring and embedded control system for factory environment.

机构信息

Department of Electrical Engineering, National Taipei University of Technology, No.1, Sec. 3, Zhongxiao E. Rd., Taipei 10608, Taiwan.

出版信息

Sensors (Basel). 2013 Dec 17;13(12):17379-413. doi: 10.3390/s131217379.

Abstract

This paper proposes a real-time method to carry out the monitoring of factory zone temperatures, humidity and air quality using smart phones. At the same time, the system detects possible flames, and analyzes and monitors electrical load. The monitoring also includes detecting the vibrations of operating machinery in the factory area. The research proposes using ZigBee and Wi-Fi protocol intelligent monitoring system integration within the entire plant framework. The sensors on the factory site deliver messages and real-time sensing data to an integrated embedded systems via the ZigBee protocol. The integrated embedded system is built by the open-source 32-bit ARM (Advanced RISC Machine) core Arduino Due module, where the network control codes are built in for the ARM chipset integrated controller. The intelligent integrated controller is able to instantly provide numerical analysis results according to the received data from the ZigBee sensors. The Android APP and web-based platform are used to show measurement results. The built-up system will transfer these results to a specified cloud device using the TCP/IP protocol. Finally, the Fast Fourier Transform (FFT) approach is used to analyze the power loads in the factory zones. Moreover, Near Field Communication (NFC) technology is used to carry out the actual electricity load experiments using smart phones.

摘要

本文提出了一种使用智能手机实时监测工厂区域温度、湿度和空气质量的方法。同时,该系统还可以检测可能出现的火焰,并对电气负载进行分析和监测。监测还包括检测工厂区域运行机器的振动。研究提出在整个工厂框架内集成 ZigBee 和 Wi-Fi 协议智能监控系统。工厂现场的传感器通过 ZigBee 协议将消息和实时感应数据传递到集成嵌入式系统。集成嵌入式系统由开源 32 位 ARM(高级精简指令集机器)核心 Arduino Due 模块构建,其中为 ARM 芯片组集成控制器内置了网络控制代码。智能集成控制器能够根据从 ZigBee 传感器接收到的数据立即提供数值分析结果。Android APP 和基于网络的平台用于显示测量结果。构建的系统将使用 TCP/IP 协议将这些结果传输到指定的云设备。最后,使用快速傅里叶变换(FFT)方法分析工厂区域的电力负载。此外,还使用近场通信(NFC)技术使用智能手机进行实际的电力负载实验。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c969/3892851/64fdb9bf0996/sensors-13-17379f49.jpg

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