一维卷积神经网络在往复式压缩机气阀故障诊断中的应用

马海辉; 余小玲; 吕倩; 叶君超

doi:10.7652/xjtuxb202204011

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一维卷积神经网络在往复式压缩机气阀故障诊断中的应用

更新时间：2025-07-09

- 一维卷积神经网络在往复式压缩机气阀故障诊断中的应用
- Application of One-Dimensional Convolutional Neural Network in Fault Diagnosis of Reciprocating Compressor Air Valve
- 西安交通大学学报 2022年56卷第4期页码：101-108
- 作者机构：
  
  1. 西安交通大学能源与动力工程学院,西安,710049
  2. 西安交通大学化学工程与技术学院,西安,710049
- 作者简介：
  
  : 2021-08-24。作者简介: 马海辉(1998—),男,硕士生
  余小玲(通信作者),女,副教授。基金项目: 国家自然科学基金资助项目(52076166)
- 基金信息：
  
  : 2021-08-24。作者简介: 马海辉(1998—),男,硕士生;余小玲(通信作者),女,副教授。基金项目: 国家自然科学基金资助项目(52076166);浙江省重点研发计划资助(2020C01119)
- DOI：10.7652/xjtuxb202204011
  中图分类号： TK05
- 网络首发：2022-04-10，
  
  纸质出版：2022
- 稿件说明：
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马海辉, 余小玲, 吕倩, 等. 一维卷积神经网络在往复式压缩机气阀故障诊断中的应用[J]. 西安交通大学学报, 2022,56(4):101-108.

Application of One-Dimensional Convolutional Neural Network in Fault Diagnosis of Reciprocating Compressor Air Valve[J]. 2022, 56(4): 101-108.
马海辉, 余小玲, 吕倩, 等. 一维卷积神经网络在往复式压缩机气阀故障诊断中的应用[J]. 西安交通大学学报, 2022,56(4):101-108. DOI： 10.7652/xjtuxb202204011.

Application of One-Dimensional Convolutional Neural Network in Fault Diagnosis of Reciprocating Compressor Air Valve[J]. 2022, 56(4): 101-108. DOI： 10.7652/xjtuxb202204011.

摘要

针对往复式压缩机气阀故障诊断问题

对气阀盖上的振动信号进行分析

提出了一种基于一维卷积神经网络(1D-CNN)的故障诊断模型。首先

将原始一维振动信号经傅里叶变换从时域转换为频域; 然后

将频域信号作为1D-CNN的输入

利用卷积层实现自适应提取特征; 最后

网络输出层利用Softmax函数实现多种故障的模式识别。在往复式压缩机故障模拟实验台上进行了气阀正常、阀片裂纹、阀片断裂、弹簧失效4种工作状况下气阀盖振动信号的测量并对提出的模型进行验证。结果表明

气阀盖上的振动信号能够明显反映气阀的工作状态

而且信号易提取、十分适合用于气阀的故障诊断; 将振动信号从时域转换成频域作为1D-CNN的输入明显地提高了故障分类的准确率; 与采用原始一维振动信号作为1D-CNN输入的模型相比

采用频域信号作为输入的故障诊断模型具有更优越的表现

准确率更高

可达100%

而且模型结构简单

能够实现端到端的快速故障诊断。

Abstract

Aiming at the fault diagnosis of reciprocating compressor air valves

a fault diagnosis model based on one-dimensional convolutional neural network(1D-CNN)is proposed for analyzing vibration signals on the air valve cover. First

the original one-dimensional vibration signal is converted from the time domain to the frequency domain via Fourier transformation; then

the frequency domain signal is used as the input of 1D-CNN to realize adaptive feature extraction by the convolutional layer; finally

the network output layer could recognize modes of multiple faults by Softmax function. At the reciprocating compressor failure simulation test bench

we have measured the valve cover vibration signal under four working conditions

namely normal air valve

valve plate crack

valve plate fracture and spring failure

and verified the proposed models. The result shows that the vibration signal on the air valve cover could obviously reflect the working state of the air valve

and the signal is easy to extract and very suitable for fault diagnosis of the air valve; vibration signals are converted from time domain to frequency domain as input of 1D-CNN

which significantly improves the accuracy of fault classification; compared with the model with original one-dimensional vibration signal as input

the fault diagnosis model with frequency domain signal as input has better performance and higher accuracy(up to 100%). Moreover

it is simple in structure

and could realize end-to-end fast fault diagnosis.

关键词

Keywords

references

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