Study On Equipment Monitoring And Fault Diagnosis Method Based On Oil Monitoring

The petrochemical industry is our country’s pillar industry, is playing the pivotal role in our country national economy development, but at the same time the petrochemical industry is a high-risk industries, has its own industry characteristics. Now in the context of the close ties of all sectors of society, many of the industry production run will depend on the products of the petrochemical industry, once the petrochemical industry as pillar industry has some problem, the relative industry and the downstream industry would be severely affected, so uses the reasonable monitoring to ensure the petrochemical equipment’s safe, steady, long team, full load and optimum running is very necessary, it is also a problem need solving urgently at present. Oil monitoring used in this paper is the use of optics, electricity, magnetism and other technological means, will gather the equipment lubricating oil sample for running physics and chemistry performance, carries the abrasive and the outside pollutant pellet and so on carries on the analysis and the monitor, to get information of equipment lubrication and wear particle, at the same time qualitative and quantitative description of the equipment wear state, evaluation of equipment working conditions, and can thus determine the fault, causation and types, predictive failure. And all this can be implemented in the case of normal operation of the equipment, to ensure continuous security equipment long life cycle work has very important significance. This paper focuses on the research object of operation features and common features, to improve the existing monitoring process. By comparison of the oil monitoring technology, established the ferrography as the core, complemented by conventional physical and chemical analysis and spectrographic oil analysis program, designed for long-term monitoring and emergency monitoring of the different monitoring process. Under the monitoring results meeting the reliable premise, minimizing the monitoring steps to improve its timeliness, to avoid wasting a lot of manpower and resources and monitoring conclusions delay problem may be caused by the process is too complicated. On the basis of the correct identification of the wear particles, quantitative analysis used trend analysis to the data sample regress-statistical analysis, use of statistical methods to establish the criteria for judging. Qualitative analysis applied grey relational analysis, the use of weights and the correlation coefficient contacts wear particle size, number and appearance characteristics of a significant degree of three parameters with equipment status. The automatic analysis of the wear particles, in order to achieve savings in Manpower Cost, improve the speed of monitoring, exclude the subjectivity of monitoring personnel. Production practice proved that it is full compliance with the actual conditions and achieved good results.