Study on diaphragm pump fault diagnosis and data correction techniques in online oil chromatography based on multilayer perceptrons

Abstract The diaphragm pump is a vital component in the online oil chromatography device for oil–gas separation, and its performance plays a crucial role in determining the accuracy and reliability of gas detection. However, over time, due to continuous use and external environmental factors, the performance of the diaphragm pump gradually declines. This degradation results in an insufficient vacuum during the degassing process, which adversely affects the accuracy of gas detection. To tackle this issue, this paper combines simulation and experimental approaches to thoroughly investigate the causes of diaphragm pump performance degradation and its theoretical lifespan. It also examines the impact of insufficient vacuum on the detection accuracy of various gases. In addition, by analyzing the current variation patterns of the diaphragm pump during operation, a multilayer perceptron (MLP) algorithm is applied to create a fault diagnosis and data error correction model for the diaphragm pump. Experimental results demonstrate that when the vacuum level surpasses 70%, the MLP model is capable of keeping the correction error for seven types of fault gases below 10%, significantly enhancing detection accuracy. This study offers valuable insights for the reliability assessment and performance optimization of online oil chromatography devices, helping to reduce maintenance costs and improve the safe operation of power transformers.