Condition monitoring based on kernel classifier ensembles

The objective of this work is the model-free diagnosis of faults of motor pumps installed on oil rigs by sophisticated kernel classifier ensembles. Signal processing of vibrational patterns delivers the features. Different kernel-based classifiers are combined in ensembles to optimize accuracy and increase robustness. A comparative study of various classification paradigms, all performing implicit nonlinear pattern mapping by kernels is done. We employ support vector machines, kernel nearest neighbor, Bayesian Quadratic Gaussian classifiers with kernels, and linear machines with kernels.