Reliability Estimation Using EM Algorithm with Censored Data: A Case Study on Centrifugal Pumps in an Oil Refinery

Abstract

Centrifugal pumps are widely employed in the oil refinery industry due to their efficiency and effectiveness in fluid transfer applications. The reliability of pumps plays a pivotal role in ensuring uninterrupted plant productivity and safe operations. Analysis of failure history data shows that bearings have been identified as critical components in oil refinery pump groups. Analyzing historical failure data for such systems is a complex task due to censored data and missing information. This paper addresses the complexity of estimating the Weibull distribution parameters using the maximum likelihood method under these conditions. The likelihood equation lacks an explicit analytical solution, necessitating numerical methods for resolution. The proposed approach presented in this article leverages the expectation maximization (EM) algorithm for estimating the Weibull distribution parameters in a real-world case study of a complex engineering system. The results demonstrate the superior performance of the EM algorithm with censored data, showcasing its ability to overcome the limitations of traditional methods and provide more accurate estimates for reliability metrics. This highlights the importance of obtaining results through these methodologies in the analysis of reliability and in facilitating more informed decision making in complex systems.