Research on fault diagnosis benchmark of large scale unit based on fuzzy analysis and information entropy

WANG Huijie; ZHANG Jianing; WANG Leiyu; ZHAO Likun

doi:10.19666/j.rlfd.201706059

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Research on fault diagnosis benchmark of large scale unit based on fuzzy analysis and information entropy

更新时间：2025-09-28

- Research on fault diagnosis benchmark of large scale unit based on fuzzy analysis and information entropy
- Thermal Power Generation Vol. 47, Issue 3, Pages: 32-37(2018)
- 作者机构：
  
  华北电力大学能源动力与机械工程学院
- 作者简介：
- 基金信息：
- DOI：10.19666/j.rlfd.201706059
  CLC： TK284.1
- Published Online：07 March 2018，
  
  Published：2018
- 稿件说明：
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WANG Huijie, ZHANG Jianing, WANG Leiyu, et al. Research on fault diagnosis benchmark of large scale unit based on fuzzy analysis and information entropy[J]. Thermal power generation, 2018, 47(3): 32-37.
DOI：

WANG Huijie, ZHANG Jianing, WANG Leiyu, et al. Research on fault diagnosis benchmark of large scale unit based on fuzzy analysis and information entropy[J]. Thermal power generation, 2018, 47(3): 32-37. DOI： 10.19666/j.rlfd.201706059.

摘要

全工况下的综合评价值是机组热力档案中故障诊断的基准值。将信息熵与模糊分析理论融合

对机组热力参数原始矩阵进行模糊分析得到评价矩阵

并利用信息熵计算多属性因素及其权重

计算出不同工况对应的综合评价值。对本文不同工况汽轮机组状态进行综合测评。将该方法得到的结果与常用的主成分分析法得到的综合评价值进行对比

结果表明:评价矩阵属性权重的计算结果准确而客观

信息熵法得到的结果客观且具有唯一性。

Abstract

Under all conditions

the comprehensive evaluation value can be used as the benchmark for fault diagnosis of thermal archives. In this study the information entropy and fuzzy theory are fused together and then introduced to the state assessment of power plant steam turbine unit under different conditions. Fuzzy analysis of the original matrix for thermal parameters can be used to obtain an evaluation matrix. And the information entropy is used to calculate the multi-attribute factors and their weights. Finally

the corresponding comprehensive evaluation values under different working conditions can be calculated. The results show that

compared with the comprehensive evaluation values obtained by the principal component analysis method

the attribute weight calculation of evaluation matrix is accurate and objective

and the results obtained by the information entropy method are objective and unique.

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