Computational intelligence for automatic diagnosis of submersible motor pump conditions in offshore oil exploration

We apply computational intelligence methods to the domain of fault diagnosis of rotating machinery, specifically submersible motor pumps used in offshore oil exploration. We propose distinct feature models to assemble a global feature pool from which the most discriminative information is filtered by feature selection. Statistically robust performance estimation for representative classifier models are used. The feature models are based on statistical parameters from the time and frequency domain and wavelet packet analysis. Feature selection is done by sequential techniques, with and without floating, applying wrapper and filter approaches. Performance estimation is based on the estimated accuracy and the area under the receiver operating characteristic curve (AUC-ROC). Experimental results are shown for 1834 vibration patterns, manually labeled by experts in the field of fault diagnosis. As representative classifiers we use the K-Nearest-Neighbor and Support Vector Machine.