基于ECA改进1D-CNN的柱塞泵故障诊断

Fault Diagnosis of Plunger Pump Based on ECA Improved 1D-CNN

往复式柱塞泵是油田作业的关键设备,其健康状况直接影响作业区的生产运行效率。针对变工况柱塞泵的复杂振动特性,提出基于高效通道注意力(Efficient Channel Attention,ECA)的一维卷积神经网络(One-dimensional Convolutional Neural Network,1D-CNN)油田柱塞泵故障诊断方法。在油田柱塞泵体关键部位安装加速度传感器,并使用测振系统采集泵前轴承等关键部位振动信号;利用一维卷积神经网络对油田柱塞泵振动监测信号进行学习,以识别柱塞泵故障特征,通过高效通道注意力(ECA)机制实现减少特征维度损失同时捕获特征通道信息交互,以提高柱塞泵故障诊断精度;借助SoftMax分类器实现振动加速度信号分析的多故障诊断。现场数据采集及试验分析证明,提出的故障诊断模型具有较强的数据特征提取能力,并在油田实际运行数据分析中取得了高性能的诊断效果。验证结果表明,该技术在柱塞泵监测应用中具有较强的鲁棒性和准确性,与其他单一深度学习相比有显著的故障特征提取和诊断优势,能够为柱塞泵及类似设备的故障诊断提供理论依据和技术指导。

Reciprocating plunger pump is a key equipment in oilfield operation,and its health directly affects the production operation efficiency of the operating block.Aimed at the complex vibration characteristics of plunger pump under variable working conditions,a fault diagnosis method for oilfield plunger pump based on ECA(efficient channel attention)improved 1D-CNN(one-dimensional convolutional neural network)was proposed.Acceleration sensors were installed in key parts of the oilfield plunger pump,and a vibration measurement system was used to collect vibration signals from key parts such as front bearing of the pump;the one-dimensional convolutional neural network was used to learn the fault characteristics of plunger pump through oilfield plunger pump vibration monitoring signals,and the efficient channel attention mechanism was used to achieve interaction of captured characteristic channel information at the same time of reducing the characteristic dimension loss,so as to improve the fault diagnosis accuracy of plunger pump;and the SoftMax classifier was used to achieve multi-fault diagnosis of vibration acceleration signal analysis.Field data collection and test analysis show that the proposed fault diagnosis model has strong data characteristic extraction ability,and has achieved high-performance diagnostic effect in actual oilfield operation data analysis.The verification results show that this technology has strong robustness and accuracy in the monitoring application of plunger pump,has significant advantages in fault feature extraction and diagnosis compared to other single deep learning methods,and provides theoretical basis and technical guidance for fault diagnosis of plunger pump and similar equipment.