Fault Detection in Heat Exchangers

Abstract

We have examined the feasibility of using artificial neural networks for the detection of faults in steady state operation of heat exchangers, and compared the results with standard statistical and nearest neighbor classification methods. Both deviations from normal states of measurements as well as physical causes of the faults were investigated. The results of using artificial neural nets and nearest neighbor classification were surprisingly sensitive and superior to discrimination methods.