Heat exchanger fouling and estimation of remaining useful life

Abstract

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 AAKR residuals were fused to develop a prognostic parameter which was used to develop a General Path Model (GPM) with Bayesian updating. The results demonstrate the successful application of this approach for the heat exchanger RUL prediction.