A CASE STUDY OF RELIABILITY ASSESSMENT FOR CENTRIFUGAL PUMPS IN A PETROCHEMICAL PLANT

Centrifugal pumps are widely used in petrochemical industry and in some instances, the number of pumps used
could easily amount to hundreds of pumps in a typical petrochemical plant. Consequently, the reliability of these
pumps essentially translates into stable and reliable plant operation as the pumps performances are critical in
ensuring continuous plant productivity. Reliability assessment for repairable equipment, which in this case
centrifugal pumps, is highly dependent upon the assumption of the state after each repair. The post repair states
can be categorized into three different states namely, ‘as good as new’, ‘as bad as old’ and the states in between.
In practice, however, the usual state of equipment after repair follows the state of ‘better than old but worse than
new’ which lies somewhere in between the two extremes. This paper focuses on the reliability assessment of the
centrifugal pumps at a refinery plant that has been in operation for more than 10 years using a more robust
process called generalized renewal process (GRP). This process has been proposed to model not only the ‘inbetween’
states but also the two extreme post repair states. A case study utilizing centrifugal pump failure data
is used as a comparative appraisal of reliability assessment between GRP, perfect renewal process (PRP) and
non-homogenous Poisson process (NHPP). The underlying distribution for time to first failure for these pumps
is assumed to follow the two-parameter Weibull distribution and the parameters for the models are estimated
using maximum likelihood estimation (MLE). The GRP solution based on the case study showed better
description of the failure distribution even with limited available failure data in contrast with other assumptions
as indicated by the likelihood values.