Reliability study of Subsea Control Module with focus on statistical methods
Master thesis
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http://hdl.handle.net/11250/2351211Utgivelsesdato
2015Metadata
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Sammendrag
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.