Application Of Bayesian Method In Risk Based Inspection

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...