1. A method for identifying a potential fault in a system, comprising: obtaining a set of training data; selecting a first kernel from a library of two or more kernels and adding the first kernel to a regression network; selecting a next kernel from the library of two or more kernels and adding the next kernel to the regression network; refining the regression network using a leave-one-out method in which the regression network is iteratively improved by removing a single kernel from the regression network and replacing the removed kernel with a replacement kernel from the library of kernels and then repeating the removing and replacing steps for the kernels of the regression network until a desired level of convergence between the training data and the regression network is achieved; and identifying a potential fault in the system using the refined regression network.