全自动生产线质量监测故障诊断系统研究

发布时间：2018-08-09 10:15

【摘要】：随着工业机器人技术、数控机床技术和电子计算机技术的日新月异,以及成组技术的应用,使自动化生产线向着柔性化程度更高的方向发展,其目标就是能够实现多品种、中小批量生产的自动化。同时这些现场设备作为现代化制造系统中最为基础的装备,其健康运行是现代制造企业能够正常运转的根本保证,设备结构的日趋复杂化、制造模式日趋集成化使得对设备所加工产品的质量监测工作及生产过程的故障诊断工作越来越重要。本着智能制造的思想,依靠线内西门子数控机床间的无缝配合及自检测技术,对原有托辊生产线进一步智能化升级改造,提高自动化、智能化水平,实现加工无人化工厂,完成整个托辊的生产制造。在线内生产过程中通过融合的传感器及PLC控制器进行对生产线加工的产品质量进行监控检测,并根据质量监控结果调整自身工作状态,实现自适应控制功能,同时完成多台西门子840D系统的协调工作,并利用串行通讯方法,实现大量数据的传输,以及通过M代码等手段提供设备间、通道间的协调,实现了生产线的柔性智能工作方式。其次分析以全自动化托辊生产线为对象,结合现阶段人工智能的研究,采用基于特征知识引导的模糊诊断技术方法,对生产线故障诊断进行整体设计,通过运用西门子Step 7编程管理软件、WinCC组态软件以及Mamdani模糊诊断思想作为开发工具,设计开发了一套全自动托辊生产线故障诊断系统,实现较好的人机界面,能对线内产品的不合格检测、设备的故障报警显示、通讯的故障中断进行快速诊断分析与定位,并同时给出故障现象、持续时间、故障原因及解决的建议措施。在WinCC AlarmControl控件中生产管理人员可通过报警回路功能轻松的获得该报警的详细信息,并能够将这些报警记录进行归档,保存在SQL Sever数据库中,从而大大的提高了工作的效率及托辊的生产质量,降低了废品率,以最大限度地降低故障给整个生产带来的损失,使托辊企业在市场竞争中获得生机与良机。
[Abstract]:With the rapid development of industrial robot technology, NC machine tool technology and electronic computer technology, as well as the application of group technology, the automatic production line is developing towards a higher degree of flexibility. The goal is to realize a variety of products. Automation of medium and small batch production. At the same time, as the most basic equipment in the modern manufacturing system, the healthy operation of these equipment is the fundamental guarantee for the normal operation of modern manufacturing enterprises, and the equipment structure is becoming more and more complicated. With the increasing integration of manufacturing mode, it is more and more important to monitor the quality of the products processed by the equipment and to diagnose the faults in the production process. In line with the idea of intelligent manufacturing, depending on the seamless cooperation and self-detection technology between Siemens NC machine tools in line, the original roller production line is further upgraded intelligently, and the automation and intelligence level is raised, and the processing of unattended chemical factory is realized. Complete the production and manufacture of the whole roller. In the process of on-line production, the product quality is monitored and tested by the fusion sensor and PLC controller. According to the quality monitoring result, the self-working state is adjusted, and the adaptive control function is realized. At the same time, the coordination of several Siemens 840D systems is completed, and a large amount of data transmission is realized by serial communication method, and the coordination between devices and channels is provided by means of M code. The flexible intelligent working mode of the production line is realized. Secondly, taking the fully automatic roller production line as the object, combining with the research of artificial intelligence at present, adopting the fuzzy diagnosis technology method based on the characteristic knowledge to design the fault diagnosis of the production line as a whole. By using Siemens Step 7 programming management software WinCC configuration software and Mamdani fuzzy diagnosis idea as the development tool, a set of automatic roller production line fault diagnosis system is designed and developed, and a better man-machine interface is realized. It can quickly diagnose and locate the failure detection of the product in line, the fault alarm of the equipment and the fault interruption of communication. At the same time, it also gives the fault phenomenon, the duration, the cause of the fault and the suggestion to solve the problem. In the WinCC AlarmControl control, the production manager can easily obtain the detailed information of the alarm through the alarm loop function, and can archive these alarm records and store them in the SQL Sever database. Therefore, the efficiency of work and the production quality of roller are greatly improved, the rate of waste products is reduced, the loss of the whole production caused by the failure is reduced to the maximum extent, and the enterprise of roller can obtain the vigor and opportunity in the market competition.
【学位授予单位】：沈阳理工大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP277;TP278

【相似文献】