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    

The multi-source signal fusion reciprocating compressor fault diagnosis method

TANG Yang1, XIAO Xiao1, GUAN Miantao1, NI Shentong1, LEI Bo2, YANG Xin3   

  1. 1. School of Mechanical and Electrical Engineering, Southwest Petroleum University, Chengdu 610500, Sichuan, China;
    2. Sichuan Changning Natural Gas Development Co., Ltd., Chengdu 610051, Sichuan, China;
    3. Sichuan Changhong Power Supply Co., Ltd., Mianyang 621000, Sichuan, China
  • Published:2023-10-19

CLC Number: 

  • TP306
[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.
[1] WU Huihong, QIAN Shuqu, LIU Yanmin, XU Guofeng, GUO Benhua. Multiobjective dynamic economic emission dispatch differential evolution algorithm based on elites cloning local search [J]. Journal of Shandong University(Engineering Science), 2021, 51(1): 11-23.
[2] QIAN Shuqu, WU Huihong, XU Guofeng, JIN Jingliang. Immune clonal evolutionary algorithm of dynamic economic dispatch considering gas pollution emission [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2018, 48(4): 1-9.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!