【作者】 田昭宇；

【导师】 杨剑锋；

【作者基本信息】 北京化工大学 ， 机械电子工程， 2010， 硕士

【摘要】 基于风险的检测方法简称为RBI方法,是一种对风险进行优化的方法,广泛应用在石油化工领域。RBI方法主要针对静设备,比如输油管道和化工容器等,通过量化具体每个设备的风险,将具有相似的失效频率和失效后果的设备进行分类,以此有针对的制定维护流程和检修计划。RBI方法力图做到在提高生产设备可靠性的同时,尽量减少企业用于设备维护的成本,降低停机检修的次数。通过实施RBI项目,企业可以将设备的安全管理和生产管理实现有效的结合,提高对整个系统的管理水平。我国的多个石化企业都初步实施了RBI项目,但是由于历史原因和特殊的国情,RBI方法在实施中遇到设备数据不足、历史数据缺失的问题,同时RBI方法无法提供动态的风险评估,这些不足影响了项目的实施效果。本论文中采用分析历史数据建立设备的寿命模型的方法,实现风险的动态评估,采用二参数威布尔分布作为设备的寿命模型；对于数据不足和数据缺失的问题,抽象为统计模型小子样模型和随机截尾模型,尝试多种算法对数据进行处理。算法中主要选择的是EM算法和属于贝叶斯方法中的MCMC算法,对于EM算法,提出基于EM算法的随机截尾寿命数据模型参数估计的通用算法,针对计算疲劳寿命常用的二参数威布尔分布,通过严格的理论推导,给出基于EM算法的寿命数据拟合方法,对于样本属于小子样情况采用结合Bootstrap法的EM算法,对于复杂分布模型,采用EM算法与Monte Carlo法相结合。对于MCMC算法,也给出对于服从威布尔分布的随机截尾数据的通用算法。接着通过使用Monte Carlo法模拟不完全疲劳寿命数据,验证EM算法和MCMC算法的效果,并对两种算法的结果使用使用紧邻算法、支持向量机等分类算法做进一步的分析,得出这两种算法的最佳适用范围,并使用现实中的轴承寿命数据作为演示,计算其可靠性。更多还原

【Abstract】 Risk based inspection, shorter from RBI, is an optimization method of risk, RBI is widely used in the Oil and Gas industries. RBI mainly approach to the static equipments, such as pipelines and chemical containers, after quantifying the risk of equipments, RBI will classify the equipments by their failure frequency and consequence, so as to develop for the maintenance plan. While improving the reliability, RBI achieves to save costs for the maintenance. The factory will integrate the security management with other business management by RBI, at last upgrades the whole management level.A number of petrochemical enterprises in China have implemented the RBI project, but due to historical reasons and national conditions, RBI encountered the loss of equipments data historical data, and RBI cannot provide dynamic risk assessment, these problems reduce the effect of the RBI project..The equipment life model based on historical data is used in the paper to assess risk dynamically. Two parameter Weibull distribution model is referred as the life model, while the small sample and random censored data are used to describe the real data in RBI project. The two main algorithms chose in the paper are EM algorithm and Bayesian method. A general method based on the EM algorithm is proposed to deal with random censoring model with various distributions. The parameter estimation method for the 2-Parameter Weibull distribution is developed.When the random censoring model is not justified, a bootstrap method is performed before using EM-algorithm, while an extension of the EM algorithm is combined with the Monte Carlo algorithm to address the intractable distribution models. The MCMC algorithm is chose in kinds of Bayesian methods to deal with random censoring model with various distributions. Then the mimic random censoring data simulated by Monte Carlo algorithm is utilized to assess the performance of two methods proposed in the paper. The result is analyzed by classification algorithms, such as KNN and SVM algorithms. Finally the reliability of real bearing is calculated based on the above conclusions更多还原