Fault diagnosis of sucker rod pumping systems based on curvelet transform and sparse multi-graph regularized extreme learning machine

Abstract

A novel approach is proposed to complete the fault diagnosis of pumping systems automatically. Fast Discrete Curvelet Transform is firstly adopted to extract features of dynamometer cards that sampled from sucker rod pumping systems, then a sparse multi-graph regularized extreme learning machine algorithm (SMELM) is proposed and applied as a classifier. SMELM constructs two graphs to explore the inherent structure of the dynamometer cards: the intra-class graph expresses the relationship among data from the same class and the inter-class graph expresses the relationship among data from different classes. By incorporating the information of the two graphs into the objective function of extreme learning machine (ELM), SMELM can force the outputs of data from the same class to be as same as possible and simultaneously force results from different classes to be as separate as possible. Different from previous ELM models utilizing the structure of data, our graphs are constructed through sparse representation instead of K-nearest Neighbor algorithm. Hence, there is no parameter to be decided when constructing graphs and the graphs can reflect the relationship among data more exactly. Experiments are conducted on dynamometer cards acquired on the spot. Results demonstrate the efficacy of the proposed approach for faults diagnosis in sucker rod pumping systems.