Kernel and random extreme learning machine applied to submersible motor pump fault diagnosis

This paper presents an extension of a comparative study of classifier architectures for automatic fault diagnosis, with a special emphasis on the Extreme Learning Machine (ELM), with and without kernel mapping. Besides the explanation of the ELM model, an attempt is made to find theoretical hints of the excellent generalization capabilities of this model, based on the findings of Cover about dichotomies and the equivalence of Mean Squared Error minimization in the high-dimensional feature spaces induced by kernels, and spaces defined by a finite sample set. The field of application is a practical problem in the context of offshore petroleum exploration where sophisticated submersible motor pumps are extensively tested before being deployed. The work juxtaposes the performance of ELM to an existing statistically sound comparison of state of the art classifier methods for a hand-crafted feature model tailored specially to the spectra of the vibrational signals of the pump. The results suggest the remarkably good generalization capability of ELM, exhibiting the highest scores for the chosen F-measure performance criterion.