Performance analysis of extreme learning machine for automatic diagnosis of electrical submersible pump conditions

This work presents a performance analysis of the Extreme Learning Machine (ELM) compared to the Support Vector Machine (SVM) and K-Nearest Neighbor (K-NN) classifiers for automatic diagnosis of machine conditions. Tests were performed using 5,314 real examples extracted from electrical submersible pumps. The vibration signal extraction was executed in laboratory and the samples were labeled by experts. Two feature extraction models were employed, statistical features from the time and frequency domains and amplitude peaks of harmonics and subharmonics of the shaft rotation frequency. Sequential feature selection was applied to improve classifier performance and to reduce dataset dimensionality. Experimental results suggest that the ELM may be used as a classification algorithm in automatic diagnosis systems. In certain scenarios, the ELM can outperform SVM regarding the quality of results and training speed.