Fault Diagnosis Model in Heat Exchangers Using ANN and SVM

Heat exchangers, often known as HEs, are essential parts of many industrial processes because they effectively transfer heat between fluids. However, these systems are susceptible to a range of issues that can compromise their performance and reliability. This study looks into the viability of using support vector machines (SVM) and artificial neural networks (ANN) for defect detection in heat exchangers operating in a steady state. Both deviations from normal measurement states and the physical causes of faults were analyzed. Comparative results demonstrate that ANN and SVM methods exhibit superior sensitivity and accuracy in fault detection compared to traditional statistical and nearest neighbor classification methods, highlighting their potential for improving the dependability and effectiveness of heat exchanger operations