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    邓姝娟, 王卫泽, 张英, 张成成, 涂善东. 基于共词分析的航空发动机系统失效案例研究[J]. 华东理工大学学报(自然科学版), 2021, 47(5): 635-646. DOI: 10.14135/j.cnki.1006-3080.20200824002
    引用本文: 邓姝娟, 王卫泽, 张英, 张成成, 涂善东. 基于共词分析的航空发动机系统失效案例研究[J]. 华东理工大学学报(自然科学版), 2021, 47(5): 635-646. DOI: 10.14135/j.cnki.1006-3080.20200824002
    DENG Shujuan, WANG Weize, ZHANG Ying, ZHANG Chengcheng, TU Shantung. Case Study of Aeroengine System Failure Based on Co-Word Analysis[J]. Journal of East China University of Science and Technology, 2021, 47(5): 635-646. DOI: 10.14135/j.cnki.1006-3080.20200824002
    Citation: DENG Shujuan, WANG Weize, ZHANG Ying, ZHANG Chengcheng, TU Shantung. Case Study of Aeroengine System Failure Based on Co-Word Analysis[J]. Journal of East China University of Science and Technology, 2021, 47(5): 635-646. DOI: 10.14135/j.cnki.1006-3080.20200824002

    基于共词分析的航空发动机系统失效案例研究

    Case Study of Aeroengine System Failure Based on Co-Word Analysis

    • 摘要: 航空发动机是多学科交叉、多组件耦合的复杂系统,其工况复杂,一旦发生故障便会造成灾难性后果。采用共词分析法将航空发动机系统失效领域内的关键词加以分类,归纳出了主要的失效形式并实现了结果可视化。研究发现考虑自相关性后的聚类分析效果更佳,且对于不同失效机理的研究更加具体和准确。对于服役过程中的常见故障,结合聚类分析结果和多维尺度分析图可以更快速地采取检查手段进行失效分析,有效避免类似事件的再次发生。

       

      Abstract: Aero-engine is a complex system with interdisciplinary and multi-component coupling, and its working conditions are complex. It can have catastrophic consequence once the failure of aero-engine occurs. Therefore, the failure analysis of aero-engine system can provide important reference for its design, maintenance and safe operation. In this work, co-word analysis method is adopted, and three multivariate analysis methods of factor analysis, cluster analysis and multidimensional scale analysis are used to classify the keywords in the field of aero-engine system failure, and the main failure forms are summarized and the results are visualized. It is found that clustering analysis with autocorrelation is better than the other two methods. Generally, compared with traditional statistical analysis or theoretical analysis, the analysis results of typical failure forms are basically consistent, but co-word analysis is more specific, detailed and accurate for the study of different failure mechanisms. The results of traditional statistical analysis only stay at the level of general quantitative analysis, and can only indicate the general failure direction for practical engineering application. Multi-dimensional scale analysis realizes visual similarity visualization of failure keywords. The closer to the origin of coordinates, the more core the failure forms are, the higher the frequency of occurrence is. For these common faults in the service process, combining cluster analysis results and multidimensional scale analysis charts, it is convenient for technicians to find out the failure reasons of faulty equipment more quickly, grasp the key points of work more accurately, and determine the protection scheme more effectively, effectively avoiding the recurrence of similar events and prolonging the service life of the engine.

       

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