Journal of Shandong University(Engineering Science) ›› 2023, Vol. 53 ›› Issue (5): 10-19.doi: 10.6040/j.issn.1672-3961.0.2022.275
• Machine Learning & Data Mining • Previous Articles
TANG Yang1, XIAO Xiao1, GUAN Miantao1, NI Shentong1, LEI Bo2, YANG Xin3
CLC Number:
[1] 窦唯. 往复压缩机气阀故障诊断的智能方法研究[D].大庆:东北石油大学,2004. DOU Wei. Research on intelligent method for fault diagnosis of reciprocating compressor valve[D]. Daqing: Northeast Petroleum University, 2004. [2] 张明,江志农. 基于多源信息融合的往复式压缩机故障诊断方法[J]. 机械工程学报, 2017, 53(23): 46-52. ZHANG Ming, JIANG Zhinong. Fault diagnosis method of reciprocating compressor based on multi-source information fusion[J]. Journal of Mechanical Engineering, 2017, 53(23): 46-52. [3] 吴宇飞. 基于多源信息融合的往复压缩机智能诊断技术研究[D]. 沈阳:东北大学, 2016. WU Yufei. Research on intelligent diagnosis technology of reciprocating compressor based on multi-source information fusion[D]. Shenyang: Northeast University, 2016. [4] HAN Dongying, TIAN Jinghui, XUE Peng, et al. A novel intelligent fault diagnosis method based on dual convolutional neural network with multi-level information fusion[J]. Journal of Mechanical Science and Technology, 2021, 35(8): 3331-3345. [5] LIU Yuwei, CHENG Yuqiang, ZHANG Zhenzhen, et al. Multi-information fusion fault diagnosis based on KNN and improved evidence theory[J]. Journal of Vibration Engineering & Technologies, 2022, 10(3): 841-852. [6] ZHAO Haiyang, WANG Jindong, LI Jay, et al. A compound interpolation envelope local mean decomposition and its application for fault diagnosis of reciprocating compressors[J]. Mech Syst Signal Process, 2018, 110: 273-295. [7] ZHANG Ying, JIN Chen, MA Bo. Fault diagnosis of reciprocating compressor using a novel ensemble empirical mode decomposition convolutional deep belief network[J]. Measurement, 2020, 156:107619. [8] 肖顺根,唐友福. 往复压缩机故障机理与诊断方法研究[M]. 沈阳:东北大学出版社, 2019. [9] 钱志勤,王志鹏,曹群,等. 基于差分进化的信息融合故障诊断方法[J]. 振动.测试与诊断, 2013, 33(202): 137-143. QIAN Zhiqin, WANG Zhipeng, CAO Qun, et al. Information fusion fault diagnosis method based on differential evolution[J]. Vibration. Test and Diagnosis, 2013, 33(202):137-143. [10] 李凌均,韩捷,李朋勇,等. 矢双谱分析及其在机械故障诊断中的应用[J]. 机械工程学报, 2011(17): 50-54. LI Lingjun, HAN Jie, LI Pengyong, et al. Vector bispectrum analysis and its application in mechanical fault diagnosis[J]. Journal of Mechanical Engineering, 2011(17): 50-54. [11] 龙霞飞. 大型风力发电机组齿轮箱智能化故障诊断方法研究[D]. 广州: 华南理工大学, 2019. LONG Xiafei. Research on intelligent fault diagnosis method of large wind turbine gearbox[D]. Guangzhou: South China University of Technology, 2019. [12] HUANG Guangbin, ZHU Qinyu, SIEW Chee Kheong. Extreme learning machine: theory and applications[J]. Neurocomputing, 2006, 70(1/2/3):489-501. [13] HUANG Guangbin, WANG Dianhui, LAN Yuan. Extreme learning machines: a survey[J]. International Journal of Machine Leaning and Cybernetics, 2011, 2(2):107-122. [14] HUANG Guangbin, SIEW Chee Kheong. Extreme learning machine: RBF network case[C] // 8th ICARCV Control, Automation, Robotics and Vision Conference, 2004.Kunming, China:IEEE, 2004:1029-1036. [15] HUANG Guangbin, SIEW Chee Kheong. Extreme learning machine with randomly assigned rbf kernels[J]. International Journal of Information Technology, 2005, 11(1):16-24. [16] FEI Shengwei, LIU Yingzhe. Fault diagnosis method of bearing utilizing GLCM and MBASA-based KELM[J]. Scientific Reports, 2022, 12(1): 17368. [17] XUE Jiankai, SHEN Bo. A novel swarm intelligence optimization approach: sparrow search algorithm[J]. Systems Science & Control Engineering, 2020, 8:22-34. [18] 孙大洪, 王发展, 刘强, 等. 基于BP和RBF神经网络的滚动轴承故障诊断比较[J]. 轴承, 2010(2): 53-56. SUN Dahong, WANG Fazhan, LIU Qiang, et al. Comparison of rolling bearing fault diagnosis based on BP and RBF neural network[J]. Bearing, 2010(2): 53-56. [19] 江志农,张进杰,马波,等. 往复式压缩机故障监测与诊断技术[M]. 北京:科学出版社,2018. [20] 张育凡. 基于蚱蜢优化和最小二乘支持向量机的电力负荷预测研究[D]. 兰州:兰州大学, 2018. ZHANG Yufan. Research on power load forecasting based on grasshopper optimization and least squares support vector machine[D]. Lanzhou: Lanzhou Univer-sity, 2018. [21] 刘志雄,梁华. 粒子群算法中随机数参数的设置与实验分析[J].控制理论与应用, 2010, 27(11): 1489-1496. LIU Zhixiong, LIANG Hua. Setting and experimental analysis of random number parameters in particle swarm optimization[J]. Control Theory and Application, 2010, 27(11): 1489-1496. |
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