Anomalous pressure detection in sampling systems based on Gramian angular field and parallel KConvNeXt
10.3969/j.issn.1005-202X.2025.09.009
- VernacularTitle:基于格拉姆角场与并行KConvNeXt的采样系统异常压力检测
- Author:
Qi ZHANG
1
;
Shenping XIAO
;
Libo NIE
;
Yuangang PENG
;
Yongbo SONG
Author Information
1. 湖南工业大学生命科学与化学学院,湖南 株洲 412007
- Publication Type:Journal Article
- Keywords:
sampling system;
anomaly detection;
Gramian angular field;
ConvNeXt network
- From:
Chinese Journal of Medical Physics
2025;42(9):1184-1190
- CountryChina
- Language:Chinese
-
Abstract:
A detection model based on Gramian angular field(GAF)and parallel KConvNeXt network is proposed for accurately detecting the abnormal conditions caused by sample needle blockage in the sampling system during the sampling,thus improving the testing accuracy and detection efficiency of automated biochemical analyzers.GAF-based method is employed to transform the time series of one-dimensional pressure signals into two-dimensional image representations.Subsequently,an improved attention mechanism integrated with a parallel dual-channel KConvNeXt network is used to classify the pressure signals,and achieves a final classification accuracy of 94.58%.The experimental results show that the proposed method can effectively capture the key characteristics of the pressure signals,offering an efficient solution for the anomalous pressure detection in biochemical analyzer sampling system and exhibiting important practical significance.
