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Reliability study of Subsea Control Module with focus on statistical methods

机译:着重于统计方法的海底控制模块可靠性研究

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摘要

The importance of data in carrying out reliability analysis cannot be over emphasized. Failure rate is the basic input for reliability assessment. Therefore, identifying a realistic estimate helps to achieve accurate results. This master thesis looks at some practical aspects and elements of statistical methods in reliability analysis using the case study. We estimated the failure rate of Subsea Control Module based on the company s database reliability record (e.g. failure times). This thesis applies available methods and models of reliability and lifetime analysis by performing functional analysis, failure analysis, and reliability assessment of the SCM. Different literature was used to understand reliability concepts and its application in various forms of required analysis. We reviewed the development cycle of statistical methods starting with pure mathematical parametric models which evolved into reliability tools (non-parametric and semiparametric models). Some of the identified statistical data analysis methods were further usedto derive the failure rate of an SCM for equipment performance assessment. We performed a failure distribution analysis for the case study using the failure and censoring times from the database record and this shows a high hazard/failure rate at the initial phase of operation. The covariate analysis revealed that there is no environmental impact on the reliability performance of the SCM but the manufacturer (brand) of the equipment has a significant impact.This work further presented the utilization of failure rates for in-dept reliability assessment of systems. Qualitative assessments like the functional failure analysis using FMECA is considered the usual method for simple systems. Failure rate is the basic data input for performing quantitative reliability assessments. We showed how it can be used to calculate the availability and frequency of system failures using the Markov approach and simplified formula.
机译:数据在执行可靠性分析中的重要性不能过分强调。失效率是可靠性评估的基本输入。因此,确定一个实际的估计值有助于获得准确的结果。本硕士论文将通过案例研究来探讨可靠性分析中统计方法的一些实际方面和要素。我们根据公司的数据库可靠性记录(例如,故障时间)估算了海底控制模块的故障率。本文通过对单片机进行功能分析,故障分析和可靠性评估,运用了现有的可靠性和寿命分析方法和模型。使用不同的文献来理解可靠性概念及其在各种形式的所需分析中的应用。我们从纯数学参数模型入手,回顾了统计方法的发展周期,这些模型已经发展成为可靠性工具(非参数模型和半参数模型)。一些确定的统计数据分析方法被进一步用于导出SCM的故障率,以进行设备性能评估。我们使用数据库记录中的故障和检查时间对案例研究进行了故障分布分析,这表明在操作的初始阶段,较高的危险/故障率。协变量分析显示,对SCM的可靠性性能没有环境影响,但设备的制造商(品牌)会产生重大影响。这项工作进一步提出了故障率在系统内部可靠性评估中的利用。定性评估,例如使用FMECA进行功能故障分析,被认为是简单系统的常用方法。失效率是进行定量可靠性评估的基本数据输入。我们展示了如何使用马尔可夫方法和简化公式将其用于计算系统故障的可用性和频率。

著录项

  • 作者

    Bitanov Askar;

  • 作者单位
  • 年度 2015
  • 总页数
  • 原文格式 PDF
  • 正文语种 eng
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