Heat Exchanger Fouling and Estimation of Remaining Useful Life

Tutpol Ardsomang, J. Wesley Hines, and Belle R. Upadhyaya
Submission Type: 
Full Paper
phmc_13_031.pdf482.91 KBSeptember 13, 2013 - 1:41pm

One of the challenges in data-driven prognostics is the
availability of degradation data for application to prognostic
methods. In real process management settings, failure data
are not often available due to the high costs of unplanned
breakdowns. This research presents a data-driven
(empirical) modeling approach for characterizing the
degradation of a heat exchanger (HX) and to estimate the
Remaining Useful Life (RUL) of its design operation. The
Autoassociative Kernel Regression (AAKR) modeling was
applied to predict the effect of fouling on the heat transfer
resistance. The result indicates that AAKR model is an
effective method to capture the HX fouling in the dynamic
process. The fouling prediction data were used to calculate
the prognostic parameters and to establish a General Path
Model (GPM) with Bayesian updating. The results
demonstrate the successful application of this approach for
the heat exchanger RUL prediction.

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Submission Keywords: 
Heat Exchanger
Submission Topic Areas: 
Data-driven methods for fault detection, diagnosis, and prognosis
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