Gas-Solid Two-phase Flow Detection Method Based on Improved MobileViT

TONG Weiguo; WANG Yongpei; LIN Lirong; CHEN Bofan; ZHONG Haizeng

doi:10.19805/j.cnki.jcspe.2026.250063

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Gas-Solid Two-phase Flow Detection Method Based on Improved MobileViT

更新时间：2026-10-10

- Gas-Solid Two-phase Flow Detection Method Based on Improved MobileViT
- Chinese Journal of Power Engineering Vol. 46, Issue 5, Pages: 154-164(2026)
- 作者机构：
  
  1. 华北电力大学自动化系,河北,保定,071003
  2. 华北电力大学河北省发电过程仿真与优化控制技术创新中心,河北,保定,071003
- 作者简介：
- 基金信息：
- DOI：10.19805/j.cnki.jcspe.2026.250063
  CLC： TB937
- Published：2026
- 稿件说明：
移动端阅览
仝卫国, 王用沛, 林礼榕, 陈波帆, 钟海增. 基于改进MobileViT的气固两相流检测方法[J]. 动力工程学报, 2026, 46(5): 154-164.
仝卫国, 王用沛, 林礼榕, 陈波帆, 钟海增. 基于改进MobileViT的气固两相流检测方法[J]. 动力工程学报, 2026, 46(5): 154-164. DOI： 10.19805/j.cnki.jcspe.2026.250063.

DOI： 10.19805/j.cnki.jcspe.2026.250063

摘要

针对气固两相流流动特性复杂导致流动参数难以准确检测的问题

结合神经网络技术对其流动过程中产生的音频信号进行分析

提出一种基于改进MobileViT的气固两相流检测方法。以轻量化网络MobileViT作为基础模型

首先通过谱减法对采集到的音频信号进行去噪

并提取其梅尔频谱图作为模型的输入;其次

在模型中引入双向空洞空间金字塔池化模块

提取特征图在水平和垂直维度上的多尺度特征信息;然后

在模型中引入全局局部空间注意力机制

增强模型对特征图关键区域特征信息的捕捉和表达能力;最后

使用细节增强卷积替换MobileViT block中的3×3标准卷积

以提取出更为丰富的特征图局部细节特征信息。结果表明:改进后的模型对6种流量条件下的气固两相流具有较好的识别效果

准确率为98.833%

较原模型提高3.166%。

Abstract

To address issues of accurately detecting flow parameters due to the complex flow characteristics of gas-solid two-phase flow

a detection method based on the improved MobileViT model was proposed

which combined neural network technology to analyze audio signals generated during the flow process of gas-solid two-phase flow. Lightweight MobileViT network was used as the base model. First

spectral subtraction was applied to denoise the collected audio signals

and their Mel spectrogram was extracted as model input. Next

a bidirectional dilated spatial pyramid pooling module was introduced into the model to extract multi-scale feature information in both horizontal and vertical dimensions. Then

a global-local spatial attention mechanism was incorporated to enhance the model's ability to capture and express the key feature information in feature map. Finally

a detail-enhanced convolution replaced the standard 3×3 convolution in the MobileViT block to extract richer local detail features from the feature map. Results show that the improved model achieves good recognition performance for gas-solid two-phase flow under six different flow conditions

with accuracy of 98.833% and 3.166% improvement over the original model.

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references

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