基于物联网的散料高精度智能称重系统及故障诊断技术研究
发布时间：2018-12-10 06:59
【摘要】：随着当代经济的快速发展,散状物料的贸易运输量急剧上升,促使对动态称重的要求越来越高,而现有的电子皮带秤在使用过程中准确度较低,长期稳定性更差,且存在数据波动,很难满足贸易计量的要求。物联网技术的发展带动了传统衡器行业的转型,运用计算机和信息技术对皮带秤工作状态进行监管,降低了人为干预引起的误差,进而确保设备的长期运行精度。本文借助物联网技术,提出了基于物联网的散料高精度智能称重系统,以此实现对称重设备的实时数据采集与监控预警。主要工作包括以下几个方面:(1)研究了皮带秤工作的特点以及现状,分别对皮带秤智能称重系统总体及其各个功能模块的需求进行分析,并详细分析各个功能模块之间的关系,进而对智能称重系统的总体框架进行设计。(2)分析了电子皮带秤的各称重误差类型及其来源,针对温度和张力这两大主要误差因素进行详细的分析,并通过实验采集数据,进一步分析主要误差影响因素之间的关系。(3)分析了传统的aiNet网络模型对于机械故障诊断的优势以及不足,在此基础上引入克隆变异算法以及K近邻理论,设计了多层免疫网络模型,通过自有诊断层和自适应诊断层互相配合,实现对新故障的有效识别。对遗传算法、传统的aiNet网络模型和改进后的多层免疫网络模型进行试验对比,找出最适合的方法。(4)详细分析与设计了散料高精度智能称重系统的各个子模块,包括数据采集与处理、故障诊断、系统管理和监督等功能。最后运用B/S架构实现了散料高精度智能称重系统。
[Abstract]:With the rapid development of modern economy, the volume of trade transportation of bulk materials increases sharply, which makes the demand for dynamic weighing more and more high. However, the existing electronic belt weighers have lower accuracy and worse long-term stability in the process of use. And there are data fluctuations, it is difficult to meet the requirements of trade measurement. The development of the Internet of things technology has led to the transformation of the traditional weighing equipment industry. The use of computer and information technology to supervise the working state of the belt scale reduces the error caused by human intervention and thus ensures the long-term operating accuracy of the equipment. In this paper, an intelligent weighing system based on the Internet of things (IOT) is proposed to realize real-time data acquisition and monitoring and early warning of symmetrical weighing equipment. The main work includes the following aspects: (1) the characteristics and current situation of belt weighing system are studied, and the requirements of the intelligent weighing system and its functional modules are analyzed respectively. The relationship between each functional module is analyzed in detail, and then the overall frame of intelligent weighing system is designed. (2) the types and sources of weighing errors of electronic belt scale are analyzed. The two main error factors, temperature and tension, are analyzed in detail, and the data are collected through experiments. Furthermore, the relationship between the main error factors is analyzed. (3) the advantages and disadvantages of the traditional aiNet network model for mechanical fault diagnosis are analyzed, and then the clonal mutation algorithm and K-nearest neighbor theory are introduced. A multi-layer immune network model is designed to identify the new faults effectively through the cooperation of the self-diagnosis layer and the adaptive diagnosis layer. The genetic algorithm, the traditional aiNet network model and the improved multi-layer immune network model are tested and compared to find out the most suitable method. (4) each sub-module of the intelligent weighing system with high precision is analyzed and designed in detail. Including data acquisition and processing, fault diagnosis, system management and supervision and other functions. Finally, the intelligent weighing system with high accuracy of bulk material is realized by using B / S architecture.
【学位授予单位】：南京理工大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TH715.1;TP311.52
本文编号：2370161
[Abstract]:With the rapid development of modern economy, the volume of trade transportation of bulk materials increases sharply, which makes the demand for dynamic weighing more and more high. However, the existing electronic belt weighers have lower accuracy and worse long-term stability in the process of use. And there are data fluctuations, it is difficult to meet the requirements of trade measurement. The development of the Internet of things technology has led to the transformation of the traditional weighing equipment industry. The use of computer and information technology to supervise the working state of the belt scale reduces the error caused by human intervention and thus ensures the long-term operating accuracy of the equipment. In this paper, an intelligent weighing system based on the Internet of things (IOT) is proposed to realize real-time data acquisition and monitoring and early warning of symmetrical weighing equipment. The main work includes the following aspects: (1) the characteristics and current situation of belt weighing system are studied, and the requirements of the intelligent weighing system and its functional modules are analyzed respectively. The relationship between each functional module is analyzed in detail, and then the overall frame of intelligent weighing system is designed. (2) the types and sources of weighing errors of electronic belt scale are analyzed. The two main error factors, temperature and tension, are analyzed in detail, and the data are collected through experiments. Furthermore, the relationship between the main error factors is analyzed. (3) the advantages and disadvantages of the traditional aiNet network model for mechanical fault diagnosis are analyzed, and then the clonal mutation algorithm and K-nearest neighbor theory are introduced. A multi-layer immune network model is designed to identify the new faults effectively through the cooperation of the self-diagnosis layer and the adaptive diagnosis layer. The genetic algorithm, the traditional aiNet network model and the improved multi-layer immune network model are tested and compared to find out the most suitable method. (4) each sub-module of the intelligent weighing system with high precision is analyzed and designed in detail. Including data acquisition and processing, fault diagnosis, system management and supervision and other functions. Finally, the intelligent weighing system with high accuracy of bulk material is realized by using B / S architecture.
【学位授予单位】：南京理工大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TH715.1;TP311.52
【参考文献】
相关期刊论文 前10条
1 李鑫;;电力设备状态监测和故障诊断技术趋势探究[J];现代经济信息;2015年21期
2 余景刚;杨泽武;;浅谈物联网技术在仓储物流中的应用[J];石油石化物资采购;2015年09期
3 陈羽;;皮带秤已经进入4.0时代——专访南京三埃工控股份有限公司董事长、“阵列式皮带秤”发明者袁延强[J];港口科技;2015年08期
4 袁延强;;阵列式皮带秤的“互联网+”模式[J];矿业装备;2015年08期
5 朱亮;李东波;何非;童一飞;袁延强;;采用改进型DENCLUE和SVM的电子皮带秤故障诊断[J];哈尔滨工业大学学报;2015年07期
6 韩成春;;并联传感器系统压力传感器故障诊断系统设计[J];计算机测量与控制;2015年07期
7 杨建业;耿建平;;基于HTML5的实时Web数据监测系统[J];桂林电子科技大学学报;2015年02期
8 本刊;;南京三埃阵列式皮带秤喜获全球首张0.2级OIML证书[J];衡器;2015年04期
9 王亚南;潘晓英;余国磊;;中小企业信息化建设ASP模式的探究[J];商;2015年10期
10 陈莉;;皮带秤数据采集与管理系统设计与实现[J];科技风;2014年06期
，本文编号：2370161
本文链接：https://www.wllwen.com/kejilunwen/ruanjiangongchenglunwen/2370161.html
