Kernel enhanced multilayer perceptron for industrial process diagnosis

We perform an empirical performance analysis of the Multilayer Perceptron applied to the fault diagnosis of motor pumps installed on oil rigs. The conventional Multilayer Perceptron architecture is compared to a recently developed enhancement of this general purpose regression/classification paradigm, using an intermediate opaque layer which maps the original patterns to a reproducing kernel Hilbert space prior to learning the usual functional mapping of the network. State of the art statistical tools are used to corroborate our hypotheses that the kernel enhanced version improves the classification performance.