Article(id=1321893299405836682, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1321893272490987695, articleNumber=null, orderNo=null, doi=null, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1751385600000, receivedDateStr=2025-07-02, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793093522168, onlineDateStr=2026-10-27, pubDate=1781712000000, pubDateStr=2026-06-18, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793093522168, onlineIssueDateStr=2026-10-27, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793093522168, creator=13701087609, updateTime=1793093522168, updator=13701087609, issue=Issue{id=1321893272490987695, tenantId=1146029695717560320, journalId=1225396423026438145, year='2026', volume='46', issue='6', pageStart='1', pageEnd='312', issueExtLink='null', onlineDate='null', pubDate='1781712000000', pubDateStr='2026-06-18', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793093515751, creator='13701087609', updateTime=1793094575775, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1321897718625194101, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1321893272490987695, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1321897718625194102, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1321893272490987695, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=163, endPage=173, ext={EN=ArticleExt(id=1321893299598774667, articleId=1321893299405836682, tenantId=1146029695717560320, journalId=1225396423026438145, language=EN, title=Research on Mining Equipment Anomaly Detection Integrating BiLSTM and an Improved Subtraction Average Optimizer, columnId=null, journalTitle=Mining Research and Development, columnName=null, runingTitle=null, highlight=null, articleAbstract=

To address the issues of low efficiency and insufficient accuracy in traditional mining equipment anomaly detection, a mining equipment anomaly detection model integrating bidirectional long short memory network and an improved subtraction average optimizer was proposed. Attention mechanism was introduced to achieve dynamic weighting of key features. Convolutional neural network was combined to extract local temporal features, and guided regularized random forest was employed to perform feature selection, thereby reducing data dimensionality and suppressing noise interference. The improved subtraction average optimizer introduced chaotic mapping initialization and golden-sine step-size adaptive adjustment strategy, which significantly enhanced the convergence speed and training stability of the model. The results show that on the mining equipment multisensor dataset and the mining machinery fault simulation dataset, the accuracy of the proposed model reaches 94.17% and 95.48%, respectively, and its comprehensive performance surpasses that of existing mainstream detection models. In ablation experiments, when all key modules are integrated, the accuracy, precision, recall, and F₁ score of the model reach 98.25%, 97.62%, 98.05% and 97.84%, respectively. In field tests on key equipment such as belt conveyors and crushers at a large open-pit mine, the detection accuracy of the model increases to 97.32%, with an average detection time of only 0.39 s. This indicates that the model can detect abnormal states of mining equipment efficiently and accurately, providing reliable technical support for ensuring mining production safety.

, authors=Zongyan Lyu, Xiwen Yuan, authorsList=Zongyan Lyu, Xiwen Yuan, authorCompany=null, correspAuthors=Xiwen Yuan, 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=1321893299850432908, articleId=1321893299405836682, tenantId=1146029695717560320, journalId=1225396423026438145, language=CN, title=融合BiLSTM与改进减法平均优化器的矿山设备异常检测研究, columnId=1226462295946085268, journalTitle=矿业研究与开发, columnName=矿山机电与矿业智能化, runingTitle=null, highlight=null, articleAbstract=

针对传统矿山设备异常检测效率低、精度不足的问题，提出融合双向长短记忆网络与改进减法平均优化器的矿山设备异常检测模型。引入注意力机制实现关键特征动态加权，结合卷积神经网络提取局部时序特征，采用引导正则化随机森林完成特征选择，降低数据维度并抑制噪声干扰。改进减法平均优化器引入混沌映射初始化与黄金正弦步长自适应调整策略，显著提升了模型的收敛速度与训练稳定性。结果表明，在矿山设备多传感器时序数据集与矿山机械故障模拟数据集中，本文所提模型的准确率分别达到94.17%和95.48%，综合性能优于现有主流检测模型；消融试验中，集成所有关键模块时，模型的准确率、精准率、召回率和F₁分数分别达到98.25%、97.62%、98.05%、97.84%。在某大型露天矿场的皮带输送机和破碎机等关键设备实测中，模型的检测准确率提升至97.32%，平均检测时间仅为0.39 s，表明该模型可高效、精准地检测矿山设备异常状态，为保障矿山生产安全提供了可靠的技术支持。

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吕宗岩（1979—），男，黑龙江齐齐哈尔人，硕士，高级工程师，主要从事矿山自动化方面的研究。E-mail:13513341232@163.com

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吕宗岩（1979—），男，黑龙江齐齐哈尔人，硕士，高级工程师，主要从事矿山自动化方面的研究。E-mail:13513341232@163.com

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