<?xml version="1.1" encoding="utf-8"?>
<article xsi:noNamespaceSchemaLocation="http://jats.nlm.nih.gov/publishing/1.1/xsd/JATS-journalpublishing1-mathml3.xsd" dtd-version="1.1" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><front><journal-meta><journal-id journal-id-type="publisher-id">UAID</journal-id><journal-title-group><journal-title>Urban Architecture and Development</journal-title></journal-title-group><issn>2995-2441</issn><eissn>2993-270X</eissn><publisher><publisher-name>Art and Design</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.61369/UAID.2025020020</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title>汽车动力智能故障诊断模型构建及检验</title><url>https://artdesignp.com/journal/UAID/3/2/10.61369/UAID.2025020020</url><author>张性伟,谢振,张赛文,代云川,海哈小东,果机布且</author><pub-date pub-type="publication-year"><year>2025</year></pub-date><volume>3</volume><issue>2</issue><history><date date-type="pub"><published-time>2025-06-20</published-time></date></history><abstract>汽车动力系统故障诊断是保障行车安全、提升运维效率的重中之重，本文聚焦智能故障诊断模型的理论构建及验证，探讨基于深度学习的多模态信号融合分析框架。通过构建包含信号感知层、特征融合层、智能诊断层及决策输出层的理论模型，并采用蒙特卡洛交叉验证与迁移学习理论模拟，证明了其在噪声干扰与工况迁移下的强泛化性与鲁棒性，以期为汽车动力系统智能化运维提供了坚实的理论支撑与方法论指导。</abstract><keywords>动力系统,故障诊断,深度学习,特征融合,图卷积网络</keywords></article-meta></front><body/><back><ref-list><ref id="B1" content-type="article"><label>1</label><element-citation publication-type="journal"><p>[1]杨明,丁能根,郑四发,等.汽车发动机故障诊断的深度学习方法[J].清华大学学报(自然科学版),2017,57(11):1183-1189.[2]Devlin,J.,Chang,M.W.,Lee,K.,&amp;amp;Toutanova,K.BERT:Pre-training​of​Deep​Bidirectional​Transformers​for​Language​Understanding.​Proceedings​of​the​2019​Conference​of​the​North​American​Chapter​of​the​Association​for​Computational​Linguistics:Human​Language​Technologies.2019.[3]Lundberg,S.M.,&amp;amp;Lee,S.I.A​Unified​Approach​to​Interpreting​Model​Predictions.Advances​in​Neural​Information​Processing​Systems,2017.[4]Snell,J.,Swersky,K.,&amp;amp;Zemel,R.Prototypical​Networks​for​Few-shot​Learning.​Advances​in​Neural​Information​Processing​Systems,2017.[5]王飞跃,曾大军,李众.平行智能：复杂系统的计算化建模、网络化控制与智能化决策[J].自动化学报,2015,41(4):605-612.</p><pub-id pub-id-type="doi"/></element-citation></ref></ref-list></back></article>
