工程科学学报 Chinese Journal of Engineering 一种改进的L-RWMK①-ELM故障诊断模型 刘星赵建印朱敏张伟 Research on an improvedRWMKE-ELM fault diagnosis model LIU Xing.ZHAO Jian-yin,ZHU Min,ZHANG Wei 引用本文： 刘星，赵建印，朱敏，张伟.一种改进的1。-RWMKE-ELM故障诊断模型[J.工程科学学报，2022,44(1)：82-94.doi: 10.13374/i.issn2095-9389.2020.07.09.001 LIU Xing,ZHAO Jian-yin,ZHU Min,ZHANG Wei.Research on an improved L-RWMKE-ELM fault diagnosis model[J].Chinese Journal of Engineering,.2022,44(1)82-94.doi:10.13374j.issn2095-9389.2020.07.09.001 在线阅读View online::https::/doi.org/10.13374j.issn2095-9389.2020.07.09.001 您可能感兴趣的其他文章 Articles you may be interested in 一种基于轻量级神经网络的高铁轮对轴承故障诊断方法 Fault diagnosis of high-speed train wheelset bearing based on a lightweight neural network 工程科学学报.2021,43(11)：1482 https:/oi.org/10.13374.issn2095-9389.2020.12.09.001 基于全局优化支持向量机的多类别高炉故障诊断 Multi-class fault diagnosis of BF based on global optimization LS-SVM 工程科学学报.2017,391)：39htps:1doi.org10.13374.issn2095-9389.2017.01.005 一种基于鲁棒随机向量函数链接网络的磨矿粒度集成建模方法 Grinding process particle size modeling method using robust RVFLN-based ensemble learning 工程科学学报.2019,41(1)：67 https:/1doi.org/10.13374.issn2095-9389.2019.01.007 形态分量分析在滚动轴承故障诊断中的应用 Application of morphological component analysis for rolling element bearing fault diagnosis 工程科学学报.2017,396：909 https::/1doi.org10.13374j.issn2095-9389.2017.06.014 基于极限学习机(ELM)的连铸坯质量预测 Quality prediction of the continuous casting bloom based on the extreme learning machine 工程科学学报.2018,40(7)：815 https:ldoi.org10.13374.issn2095-9389.2018.07.007 基于一维卷积特征与手工特征融合的集成超限学习机心跳分类方法 Ensemble extreme learning machine approach for heartbeat classification by fusing Id convolutional and handcrafted features 工程科学学报.2021,43(9：外1224 https:/1doi.org/10.13374.issn2095-9389.2021.01.12.005一种改进的l p -RWMKE-ELM故障诊断模型 刘星 赵建印 朱敏 张伟 Research on an improved l p -RWMKE-ELM fault diagnosis model LIU Xing, ZHAO Jian-yin, ZHU Min, ZHANG Wei 引用本文: 刘星, 赵建印, 朱敏, 张伟. 一种改进的l p -RWMKE-ELM故障诊断模型[J]. 工程科学学报, 2022, 44(1): 82-94. doi: 10.13374/j.issn2095-9389.2020.07.09.001 LIU Xing, ZHAO Jian-yin, ZHU Min, ZHANG Wei. Research on an improved l p -RWMKE-ELM fault diagnosis model[J]. Chinese Journal of Engineering, 2022, 44(1): 82-94. doi: 10.13374/j.issn2095-9389.2020.07.09.001 在线阅读 View online: https://doi.org/10.13374/j.issn2095-9389.2020.07.09.001 您可能感兴趣的其他文章 Articles you may be interested in 一种基于轻量级神经网络的高铁轮对轴承故障诊断方法 Fault diagnosis of high-speed train wheelset bearing based on a lightweight neural network 工程科学学报. 2021, 43(11): 1482 https://doi.org/10.13374/j.issn2095-9389.2020.12.09.001 基于全局优化支持向量机的多类别高炉故障诊断 Multi-class fault diagnosis of BF based on global optimization LS-SVM 工程科学学报. 2017, 39(1): 39 https://doi.org/10.13374/j.issn2095-9389.2017.01.005 一种基于鲁棒随机向量函数链接网络的磨矿粒度集成建模方法 Grinding process particle size modeling method using robust RVFLN-based ensemble learning 工程科学学报. 2019, 41(1): 67 https://doi.org/10.13374/j.issn2095-9389.2019.01.007 形态分量分析在滚动轴承故障诊断中的应用 Application of morphological component analysis for rolling element bearing fault diagnosis 工程科学学报. 2017, 39(6): 909 https://doi.org/10.13374/j.issn2095-9389.2017.06.014 基于极限学习机(ELM)的连铸坯质量预测 Quality prediction of the continuous casting bloom based on the extreme learning machine 工程科学学报. 2018, 40(7): 815 https://doi.org/10.13374/j.issn2095-9389.2018.07.007 基于一维卷积特征与手工特征融合的集成超限学习机心跳分类方法 Ensemble extreme learning machine approach for heartbeat classification by fusing 1d convolutional and handcrafted features 工程科学学报. 2021, 43(9): 1224 https://doi.org/10.13374/j.issn2095-9389.2021.01.12.005