基于油液分析技术的设备监测与故障诊断方法研究

发布时间：2019-06-17 13:14

【摘要】：石化行业是我国的支柱产业,在我国国民经济的发展中起着举足轻重的作用,但同时石化行业又是一个高风险的行业,有着自己的行业特点,在当今社会各行各业密切联系的背景下,许多行业的生产运行都有赖于石化行业的产品,一旦作为支柱行业的石化行业出现问题,与之相关的行业和下游产业必然会受到严重影响,因此采用合理的监测技术保证石化机械设备安、稳、长、满、优的运行是十分必要的,也是当前迫切需要解决的问题。本文采用的油液监测技术是利用光学、电学、磁学等技术手段,对采集到的设备润滑油样品的理化指标,所携带磨粒和外界污染物颗粒等进行分析和监测,从而获得设备的润滑状态和磨粒信息,同时可以定性和定量的描述设备的磨损状态,评价设备工况,并可以据此确定故障部位、原因和类型,预测故障。而这一切均可以在设备正常运行的情况下实现,对保证设备长周期连续安全工作有着十分重要的意义。本文针对所研究的对象的运行特点和常见的故障特征,对现有的监测流程进行了改进。通过对各项油液监测技术对比后,确立了以铁谱分析技术为核心,辅之以常规理化分析和原子光谱分析技术的监测方案,并为长期监测和突发性监测设计了不同的监测流程,使监测结果在满足可靠性的前提下,尽可能的减少监测步骤,提高其时效性,避免造成人力物力的大量浪费和可能因为流程过于复杂造成监测结论延误的问题。并在对磨粒正确识别的基础上对定量分析采用趋势分析法,进行数据样本回归统计,利用统计学的方法建立判断标准,对定性分析则采用灰色关联度分析法,利用权重和关联系数将磨粒的尺寸、数量和形貌特征显著程度三个参数与设备状态联系起来,实现磨粒的自动分析,以达到节约人力成本,提高监测速度,排除监测人员主观性的目的,经实践检验后,与实际情况完全符合,取得了良好的效果。
[Abstract]:Petrochemical industry is the pillar industry of our country, which plays an important role in the development of our national economy, but at the same time, petrochemical industry is also a high-risk industry, which has its own industry characteristics. Under the background of the close relationship between various industries in today's society, the production and operation of many industries depend on the products of petrochemical industry, once there are problems in the petrochemical industry as a pillar industry. The related industries and downstream industries are bound to be seriously affected, so it is very necessary to adopt reasonable monitoring technology to ensure the safe, stable, long, full and excellent operation of petrochemical machinery and equipment, and it is also an urgent problem to be solved at present. The oil monitoring technology used in this paper uses optical, electrical, magnetic and other technical means to analyze and monitor the physical and chemical indexes of the collected equipment lubricating oil samples, the abrasive particles and external pollutant particles, so as to obtain the lubrication state and abrasive particle information of the equipment. At the same time, the wear state of the equipment can be described qualitatively and quantitatively, the equipment working conditions can be evaluated, and the fault location, cause and type can be determined. Predict the failure. All of this can be realized under the condition of normal operation of the equipment, which is of great significance to ensure the continuous and safe work of the equipment for a long period of time. In this paper, the existing monitoring flow is improved according to the running characteristics and common fault characteristics of the studied objects. Through the comparison of various oil monitoring technologies, the monitoring scheme with ferrography analysis technology as the core, supplemented by conventional physical and chemical analysis and atomic spectrum analysis technology is established, and different monitoring processes are designed for long-term monitoring and sudden monitoring, so that the monitoring results can be reduced as much as possible and their timeliness can be improved as much as possible under the premise of satisfying reliability. Avoid massive waste of human and material resources and delays in monitoring conclusions that may be caused by excessive complexity of the process. On the basis of correct identification of abrasive particles, the trend analysis method is used for quantitative analysis, the data sample regression statistics is carried out, the judgment standard is established by statistical method, and the grey correlation degree analysis method is used for qualitative analysis. The three parameters of abrasive size, quantity and morphology are connected with the equipment state by using weight and correlation coefficient, so as to realize the automatic analysis of abrasive particles in order to save the labor cost. After practical test, the purpose of improving the monitoring speed and excluding the subjectivity of the monitoring personnel is in good agreement with the actual situation, and good results have been obtained.
【学位授予单位】：太原理工大学
【学位级别】：硕士
【学位授予年份】：2012
【分类号】：TH165.3

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