Please wait a minute...
Frontiers of Mechanical Engineering

ISSN 2095-0233

ISSN 2095-0241(Online)

CN 11-5984/TH

Postal Subscription Code 80-975

2018 Impact Factor: 0.989

Front Mech Eng Chin    2010, Vol. 5 Issue (2) : 171-175    https://doi.org/10.1007/s11465-009-0091-0
RESEARCH ARTICLE
Trend prediction technology of condition maintenance for large water injection units
Xiaoli XU(), Sanpeng DENG
China Equipment and Maintenance Engineering Society, Beijing 100007, China
 Download: PDF(226 KB)   HTML
 Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks
Abstract

Trend prediction technology is the key technology to achieve condition-based maintenance of mechanical equipment. Large-sized water injection units are key equipment in oilfields. The traditional preventive maintenance is not economical and cannot completely avoid vicious accidents. To ensure the normal operation of units and save maintenance costs, trend prediction technology is studied to achieve condition-based maintenance for water injection units. The main methods of the technology are given, the trend prediction method based on neural network is put forward, and the expert system based on the knowledge is developed. The industrial site verification shows that the proposed trend prediction technology can reflect the operating condition trend change of the water injection units and provide technical means to achieve condition-based predictive maintenance.

Keywords water injection units      condition-based maintenance      trend prediction     
Corresponding Author(s): XU Xiaoli,Email:xuxiaoli@bistu.edu.cn   
Issue Date: 05 June 2010
 Cite this article:   
Xiaoli XU,Sanpeng DENG. Trend prediction technology of condition maintenance for large water injection units[J]. Front Mech Eng Chin, 2010, 5(2): 171-175.
 URL:  
https://academic.hep.com.cn/fme/EN/10.1007/s11465-009-0091-0
https://academic.hep.com.cn/fme/EN/Y2010/V5/I2/171
Fig.1  Main maintenance ways of large- or medium-sized water injection mechanical equipment
Fig.2  Main trend prediction methods
Fig.3  Neural network static prediction curve of large water injection units
Fig.4  Neural network dynamic prediction curve of large water injection units
Fig.5  Intelligent predictive maintenance structure of expert system for water injection units
Fig.6  Flow chart of knowledge processing (KPS) of water injection units
1 Xu X L, Gu Y H, Shi Y C, Asakura T, Zhang S. Virtual instrument system of condition monitoring and fault diagnosis. ISIST'2004 , 2004, 1: 1061–1066
Xu X L, Gu Y H, Shi Y C, Asakura T, Zhang S. Virtual instrument systemof condition monitoring and fault diagnosis. ISIST'2004, 2004, 1: 1061–1066
Xu X L, Zuo Y B, Zhu C M. A variable-weight neural network combinedpredicting model to the trend predicting of the condition developmentof the large-sized rotary sets. WMSCI 2006, 2006, III: 304–307
2 Xu X L, Zuo Y B, Zhu C M. A variable-weight neural network combined predicting model to the trend predicting of the condition development of the large-sized rotary sets. WMSCI 2006 , 2006, III: 304–307
3 Chen J C. Machine learning for information retrieval: neural networks, symbolic learning, and genetic algrithms. JASIS , 1995, 46(3): 194–216
doi: 10.1002/(SICI)1097-4571(199504)46:3<194::AID-ASI4>3.0.CO;2-S
Chen J C. Machine learning for information retrieval: neural networks,symbolic learning, and genetic algrithms. JASIS, 1995, 46(3): 194–216

doi: 10.1002/(SICI)1097-4571(199504)46:3<194::AID-ASI4>3.0.CO;2-S
4 Xu X L, Zuo Y B, Wen H Z, Wang X B, Shi Y C. Application of rapid knowledge acquisition method in intelligent diagnosis instrument. ISTAI’2006 , 2006, 2: 1034–1037
Xu X L, Zuo Y B, Wen H Z, Wang X B, Shi Y C. Application of rapid knowledgeacquisition method in intelligent diagnosis instrument. ISTAI'2006, 2006, 2: 1034–1037
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed