Fault Diagnosis Method for Submersible Reciprocating Pumping Unit Based on Deep Belief Network

Abstract

A fault diagnosis method based on deep belief network (DBN) is to solve the high fault rate of a submersible reciprocating pumping unit, and to address the difficulties in measurement of downhole operation parameters. The running current of the submersible motor is obtained directly through the ground equipment. The running current is used as the characteristic parameter of the operation status of the submersible reciprocating pumping unit. The vector that is extracted from the running current is used as the input data for the fault diagnosis model. The DBN is firstly trained by the original currents, and then the fault feature's gradual extraction is realized through the multi-layered structure, thereby allowing the fault diagnosis of the submersible reciprocating pumping unit. In the experiment, the fault diagnosis model is tested by simulation samples. Results show that the model can extract the fault feature from the running currents of the submersible motor and implement the fault diagnosis effectively.