Study on Fault Diagnosis Expert System of Reciprocating Compressors

被引:0
|
作者
Liu, Ya-Jin [1 ]
Guo, Jiang [2 ]
Song, Qi [1 ]
机构
[1] Kunming Univ Sci & Technol, Fac Elect Power Engn, Kunming 650500, Yunnan, Peoples R China
[2] Wuhan Univ, Coll Power & Mech Engn, Wuhan 430072, Hubei, Peoples R China
关键词
Reciprocating Compressor; Fault Diagnosis; Expert System;
D O I
暂无
中图分类号
TH [机械、仪表工业];
学科分类号
0802 ;
摘要
Reciprocating compressor is the key mechanical equipment widely used in petroleum chemical industry. In order to prevent and reduce equipment failure, improve equipment operation safety and stability, it is very necessary to research on fault diagnosis technology of reciprocating compressor. This paper mainly researches the application of expert system in fault diagnosis of reciprocating compressor. The fault diagnosis expert system of reciprocating compressor is designed and systematization is realized. The expert system function will be gradually strengthened with the use and learning.
引用
收藏
页码:191 / 194
页数:4
相关论文
共 50 条
  • [1] Study of Mine Air Compressors' Remote Monitoring and Fault Diagnosis Expert System
    Wang, Hui
    Wang, Haijian
    Zhao, Di
    Yang, Lin
    [J]. HYDRAULIC EQUIPMENT AND SUPPORT SYSTEMS FOR MINING, 2013, 619 : 81 - +
  • [2] Fault-Diagnosis for Reciprocating Compressors Using Big Data
    Keerqinhu
    Qi, Guanqiu
    Tsai, Wei-Tek
    Hong, Yi
    Wang, Wenxiang
    Hou, Guangxin
    Zhu, Zhiqin
    [J]. PROCEEDINGS 2016 IEEE SECOND INTERNATIONAL CONFERENCE ON BIG DATA COMPUTING SERVICE AND APPLICATIONS (BIGDATASERVICE 2016), 2016, : 72 - 81
  • [3] Fault Diagnosis of Reciprocating Compressors Valve Based on Cyclostationary Method
    王雷
    王奉涛
    赵俊龙
    马孝江
    [J]. Journal of Donghua University(English Edition), 2011, 28 (04) : 349 - 352
  • [4] Bispectrum Analysis of Motor Current Signals for Fault Diagnosis of Reciprocating Compressors
    Naid, Abdelhamid
    Gu, Fengshou
    Shao, Yimin
    Al-Arbi, Salem
    Ball, Andrew
    [J]. DAMAGE ASSESSMENT OF STRUCTURES VIII, 2009, 413-414 : 505 - +
  • [5] An Intelligent Fault Diagnosis Method for Reciprocating Compressors Based on LMD and SDAE
    Liu, Yang
    Duan, Lixiang
    Yuan, Zhuang
    Wang, Ning
    Zhao, Jianping
    [J]. SENSORS, 2019, 19 (05)
  • [6] An evaluating study ofusing thermal imaging and convolutional neural network for fault diagnosis of reciprocating compressors
    Deng, Rongfeng
    Tang, Xiaoli
    Song, Lin
    Abdulmumeer, Abdullahi
    Gu, Fengshou
    Ball, Andrew D.
    [J]. International Journal of COMADEM, 2020, 23 (04): : 23 - 26
  • [7] The study of the system of an condition monitoring and fault diagnosis for a reciprocating compressor
    Zhang, Lin
    You, Yikuang
    Wang, Zhenghong
    Zhao, Peng
    Zhang, Jianhua
    Xue, Lei
    [J]. ENGINEERING STRUCTURAL INTEGRITY: RESEARCH, DEVELOPMENT AND APPLICATION, VOLS 1 AND 2, 2007, : 373 - 376
  • [8] Fault-diagnosis for reciprocating compressors using big data and machine learning
    Qi, Guanqiu
    Zhu, Zhiqin
    Erqinhu, Ke
    Chen, Yinong
    Chai, Yi
    Sun, Jian
    [J]. SIMULATION MODELLING PRACTICE AND THEORY, 2018, 80 : 104 - 127
  • [9] Research on a small sample feature transfer method for fault diagnosis of reciprocating compressors
    Tang, Yang
    Xiao, Xiao
    Yang, Xin
    Lei, Bo
    [J]. JOURNAL OF LOSS PREVENTION IN THE PROCESS INDUSTRIES, 2023, 85
  • [10] Object-Based Thermal Image Segmentation for Fault Diagnosis of Reciprocating Compressors
    Deng, Rongfeng
    Lin, Yubin
    Tang, Weijie
    Gu, Fengshou
    Ball, Andrew
    [J]. SENSORS, 2020, 20 (12) : 1 - 11