Article(id=1319244966879146047, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1319244966216446015, articleNumber=null, orderNo=null, doi=10.19620/j.cnki.1000-3703.20250709, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=1789056000000, revisedDateStr=2026-09-11, acceptedDate=null, acceptedDateStr=null, onlineDate=1792462110489, onlineDateStr=2026-10-20, pubDate=1790179200000, pubDateStr=2026-09-24, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1792462110489, onlineIssueDateStr=2026-10-20, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1792462110489, creator=13701087609, updateTime=1792462110489, updator=13701087609, issue=Issue{id=1319244966216446015, tenantId=1146029695717560320, journalId=1189621681917173762, year='2026', volume='', issue='9', pageStart='1', pageEnd='62', issueExtLink='null', onlineDate='null', pubDate='1790179200000', pubDateStr='2026-09-24', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1792462110332, creator='13701087609', updateTime=1792735854454, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1320393132613681559, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1319244966216446015, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1320393132613681560, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1319244966216446015, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=53, endPage=62, ext={EN=ArticleExt(id=1319244967134998593, articleId=1319244966879146047, tenantId=1146029695717560320, journalId=1189621681917173762, language=EN, title=Research on the Application of Fault Diagnosis of Automotive Transmission Bearings Based on Artificial Intelligence Model, columnId=1319244967055306816, journalTitle=Automobile Technology, columnName=Special Topic on Lateral Stability and Motion Tracking Control Technology for Steer-by-Wire Vehicles, runingTitle=null, highlight=null, articleAbstract=

To address the issues of premature convergence and insufficient parameter adaptability of the Rime Optimization Algorithm (RIME) when dealing with high-dimensional mixed variables, as well as the problem of the LSTM's reliance on manually set hyperparameters which affect the diagnostic performance, a fault diagnosis method based on the improved RIME algorithm optimizing LSTM is proposed and applied to the multi-condition fault identification of automotive gearbox rolling bearings. Firstly, this study makes three key improvements to the original RIME algorithm to overcome its premature convergence and insufficient parameter adaptability when dealing with high-dimensional mixed variables: introducing an adaptive parameter adjustment strategy based on population diversity to balance the exploration and exploitation capabilities of the algorithm; designing a Levy flight global disturbance mechanism to enhance the ability to escape from local optima; proposing a mixed search space encoding scheme to efficiently handle continuous and discrete hyperparameters. Simulation experiments on standard test functions show that the improved RIME algorithm has significantly improved convergence speed and solution accuracy. Secondly, on this basis, the improved RIME algorithm is applied to optimize the key hyperparameters of LSTM, and an improved RIME-LSTM fault diagnosis model is constructed. Finally, experimental research is conducted using the self-built comprehensive simulation test bench for automotive gearbox rolling bearing faults. The experimental results show that the proposed improved RIME-LSTM fault diagnosis method has a diagnostic accuracy rate of up to 99.98% and an F₁ score of 99.75%, which is significantly superior to the comparison methods such as PSO-LSTM and GWO-LSTM. At the same time, the noise robustness test shows that this method can still maintain the highest diagnostic performance under different noise levels.

, authors=Shikai Deng¹, Xianqiu Wang², authorsList=Shikai Deng, Xianqiu Wang, authorCompany=null, correspAuthors=Xianqiu Wang, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1319244967432794179, articleId=1319244966879146047, tenantId=1146029695717560320, journalId=1189621681917173762, language=CN, title=基于人工智能模型的汽车变速箱轴承故障诊断应用研究, columnId=1319244967223078978, journalTitle=汽车技术, columnName=线控转向车辆横向稳定性与运动跟踪控制技术专题, runingTitle=null, highlight=null, articleAbstract=

针对霜冰优化算法（RIME）在处理高维混合变量时易早熟收敛、参数自适应能力不足，以及长短期记忆（LSTM）网络超参数依赖人工设定而影响诊断性能的问题，提出了一种基于改进RIME算法优化LSTM网络的故障诊断方法，并应用于汽车变速箱滚动轴承的多工况故障识别。首先，对原始RIME算法进行3项关键改进以克服其早熟收敛及处理高维混合变量时参数自适应能力不足的缺陷：引入基于种群多样性的自适应参数调整策略以平衡算法探索与开发能力；设计莱维飞行全局扰动机制以增强跳出局部最优的能力；提出混合搜索空间编码方案以高效处理连续与离散超参数。标准测试函数上的仿真试验表明，改进后的RIME算法在收敛速度与求解精度上均获得显著提升。其次，在此基础上，将改进RIME算法应用于优化LSTM网络的关键超参数，构建了改进RIME-LSTM故障诊断模型。最后，利用自建汽车变速箱滚动轴承故障综合模拟试验台进行试验研究，试验结果表明，所提改进RIME-LSTM故障诊断方法的诊断准确率达99.98%，F₁分数达到99.75%，其性能显著优于PSO-LSTM、GWO-LSTM等对比方法。同时，噪声鲁棒性测试显示，在不同噪声水平干扰下，该方法依然能够保持最高的诊断性能。

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