Electric Submersible Pump Fault Diagnosis Based on Laplacian Eigenmaps and Weighted Extreme Learning Machine

Abstract

Electric submersible pump (ESP) oil production technology is widely used in non-flowing high-yield wells and high water-cut wells, but equipment faults are prone to occur during operation, and subsequent maintenance may trigger long-term downtime, which may cause incalculable economic losses. At present, ESP fault diagnosis mainly depends on the experience of field technicians, and automatic diagnosis and analysis cannot be done quickly and timely. Therefore, this paper proposes an ESP fault diagnosis model based on Laplacian eigenmaps and weighted extreme learning machine. In response to the serious imbalance in the data collected by ESP, firstly, a fault diagnosis model is established using a weighted extreme learning machine; Then, to solve the problems of insufficient algorithm learning, high computational costs caused by weighted strategies, and poor performance in applying to high-dimensional feature spaces, the Laplacian eigenmaps method is introduced to further optimize the model; finally, the effectiveness of the proposed method was validated on the TE chemical process dataset, and the practicality of the algorithm was experimentally validated on the real-time fault dataset of electric submersible pump. The results show that the classification average accuracy, maximum accuracy, and G-mean of the algorithm proposed in this paper are improved by more than 10% on average compared with those of the support vector machine, decision tree, backpropagation (BP) algorithm, extreme learning machine, and weighted extreme learning machine, thus confirming the effectiveness of the proposed method.

Keywords

• unbalanced dataset
• fault diagnosis
• weighted extreme learning machine
• manifold learning
• laplacian eigenmaps