测试技术学报2024，Vol.38Issue(4)：441-447,7.DOI:10.3969/j.issn.1671-7449.2024057
基于非刚性特征的发动机积碳程度判别模型
The Discriminative Model of Carbon Deposit Degree of Engine Based on Non-Rigid Features
摘要
Abstract
Long-term accumulation of carbon deposit in car engines can result in decreased engine power,increased fuel consumption,and diminished emission performance,highlighting the critical importance of timely engine detection and cleaning for effectively mitigating the impact of carbon deposit.A novel model based on non-rigid features has been proposed to discriminate the degree of carbon deposit.Firstly,deformable convolution is employed in the model to adjust the offset position and amplitude of the convolu-tion kernel,enhancing the effective receptive field of the network and extracting non-rigid feature informa-tion.Subsequently,neurons are weighted based on the correlation of pixels within the kernel region using an adaptive exponential metric pooling kernel to capture more precise feature information.Finally,a fea-ture improvement module based on a self-attention mechanism is incorporated to extract comprehensive contextual information from feature maps.The model's test accuracy after experimental testing is 87.1%,and the indexes have increased by 2.5%on average compared to the original model.Demonstrat-ing the capability of our proposed method to extract effective features for carbon deposit degree discrimina-tion.The approach has the potential to theoretically justify the widespread promotion of the degree model discrimination for carbon deposit.关键词
积碳程度识别/非刚性特征/可变形卷积/下采样/自注意机制Key words
discriminate the degree of carbon deposit/non-rigid features/deformable convolution/downs-ample/self-attention mechanism分类
信息技术与安全科学引用本文复制引用
黄倩,王召巴,陈香,张航佳,陈友兴..基于非刚性特征的发动机积碳程度判别模型[J].测试技术学报,2024,38(4):441-447,7.基金项目
山西省回国留学人员科研资助项目(2022-145) （2022-145）
中北大学研究生科技立项资助课题(2022180506) （2022180506）
