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Advisor(s)
Abstract(s)
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
Description
This work is funded by National Funds through the FCT—Foundation for Science and Technology, I.P., within the scope of the project Ref. UIDB/05583/2020. Furthermore, we would like to thank the Research Centre in Digital Services (CISeD) and the Instituto Politécnico de Viseu for their support.
Keywords
Reliability estimation EM algorithm Censored data Weibull distribution Industrial equipment Maintenance optimization Failure analysis Proactive maintenance
Citation
Silva, J., Vaz, P., Martins, P., & Ferreira, L. (2023). Reliability Estimation Using EM Algorithm with Censored Data: A Case Study on Centrifugal Pumps in an Oil Refinery. Applied Sciences, 13(13). https://doi.org/10.3390/app13137736
Publisher
MDPI