Fault diagnosis of natural gas pumping unit based on machine learning

Abstract

This paper presents a method for fault detection of natural gas pumping unit. It is a very difficult object for diagnosis. A lot of combinations of technical equipment, different operational conditions, and other factors require design and implementation of reliable diagnosis methods. Acoustic signal based fault diagnosis of natural gas pumping units is well known and widely used in a number of applications. Statistical modeling and frequency analysis are among the most popular. In this paper, we share our experience in the use of the classification model based on an artificial multilayered dense feed forward neural network and a deep learning approach for software-implemented diagnosis of a GTK-25-i type of pumping unit. The paper reports the overall accuracy of 0.98 and minimum F1-score of 0.8. This is competitive compared to the latest industry research findings.