基于神经网络的TRT故障诊断技术研究

发布时间：2018-05-20 04:29

本文选题：TRT + 神经网络　；参考：《上海交通大学》2012年硕士论文

【摘要】：机械设备是企业生产的物质基础,是生产力的重要组成部分。在工业生产中,随着时间的推移,各种设备必然会产生各种形式的磨损,以及导致设备精度和效率的降低,从而使产品质量下降,严重的还会造成设备事故,为此开展对设备故障及性能诊断技术的课题研究意义重大。故障及性能诊断是一门快速发展的交叉学科,它集测试技术、软件工程、计算机技术、信号处理、模式识别、人工智能、决策科学、信息科学等众多现代科学技术于一体,成为既注重理论研究,又重视实际应用的现代工程科学,并逐步形成一个体系完整、理论严谨且具有重大工程意义的新学科。从当前我国大多数企业对设备故障的处理体制来看,基本上都采用了习惯的“定期维修”和“事后维修”两种方式。定期维修虽能发现一些早期故障,防止一部分突发事故的发生,但会因为对不需要维护的设备过频更换零部件而造成过剩维修,形成一些不必要的浪费。对于事后维修而言,任何异常工况和突发故障导致的停机检修和生产节拍的停顿,都必然造成生产工序的积压,严重地影响生产计划的顺利完成。因此需要在生产过程中及早地对设备进行故障诊断,做到“先人一步发现故障,先故障之前消除隐患”。本课题以莱芜钢铁股份公司能源动力厂5#高炉煤气余热余压能量回收透平发电装置(Blast-Furnace Top pressure Recovery Turbine Unit,简称TRT)的工况监测与故障诊断为研究内容,研究的目的是要根据TRT在各种工况下表现出来的振动、噪声、温度、液压、转子、转速、气味、泄露等所有规律特征信息去综合分析和识别设备工作状态、故障类型和故障的严重程度,最终得到对修复故障有重要指导作用的诊断结论。本文在分析TRT常见的故障机理基础上,深入研究了神经网络技术的原理方法和应用技术特点,结合故障特点找出与其相对应的特征量,构建了TRT故障诊断系统的神经网络模型,并针对TRT透平转子故障样本进行了神经网络训练,基于VB软件实现了TRT系统的故障诊断界面开发。本文应用故障诊断技术实现了对机组的保护,避免了因高炉顶压瞬间增大而导致的不必要停车,为保证高炉TRT长期安全、稳定、顺行以及提高经济效益等提供了有力支持。
[Abstract]:Mechanical equipment is the material basis of enterprise production and an important part of productivity. In industrial production, with the passage of time, various kinds of equipment will inevitably produce various forms of wear and tear, and will lead to the reduction of equipment precision and efficiency, thus leading to a decline in product quality and serious equipment accidents. Therefore, it is of great significance to research the technology of equipment fault and performance diagnosis. Fault and performance diagnosis is a rapidly developing interdisciplinary subject. It integrates testing technology, software engineering, computer technology, signal processing, pattern recognition, artificial intelligence, decision science, information science and so on. It has become a modern engineering science which pays attention to both theoretical research and practical application, and gradually forms a new discipline with complete system, rigorous theory and great engineering significance. According to the current system of handling equipment failures in most enterprises in our country, two methods of "regular maintenance" and "afterwards maintenance" are basically adopted. Although periodic maintenance can find some early faults and prevent some sudden accidents from happening, it will cause excessive maintenance because of the excessive replacement of parts and components for the equipment that does not need maintenance, resulting in some unnecessary waste. For the maintenance after the event, any abnormal working conditions and sudden failures will inevitably cause the backlog of production procedures, which will seriously affect the smooth completion of production planning. Therefore, it is necessary to diagnose the equipment as early as possible in the process of production, so as to "find the fault first and eliminate the hidden trouble before the failure". In this paper, the monitoring and fault diagnosis of blast furnace gas residual heat residual pressure recovery turbine generator unit, Blast-Furnace Top pressure Recovery Turbine Unit, is taken as the research content in Laiwu Iron and Steel Co., Ltd. The purpose of the study is to comprehensively analyze and identify the working state of the equipment according to the characteristic information of vibration, noise, temperature, hydraulic pressure, rotor, speed, smell, leakage and so on, which are shown by TRT under various working conditions. Finally, the fault type and the severity of the fault can be used as an important guide for fault diagnosis. On the basis of analyzing the common fault mechanism of TRT, this paper deeply studies the principle, method and application technical characteristics of neural network technology, finds out the corresponding characteristic quantity according to the fault characteristic, and constructs the neural network model of TRT fault diagnosis system. The neural network training for the fault sample of TRT turbine rotor is carried out, and the fault diagnosis interface of TRT system is developed based on VB software. In this paper, the fault diagnosis technology is used to protect the unit and avoid the unnecessary stop caused by the instantaneous increase of the top pressure of the blast furnace, which provides a strong support for ensuring the long-term safety, stability, smooth running of the blast furnace TRT and improving the economic benefit.
【学位授予单位】：上海交通大学
【学位级别】：硕士
【学位授予年份】：2012
【分类号】：TH165.3

【引证文献】