| 引用本文: | 李永德,李红伟,张炳成,等.融合粗糙集与神经网络的燃气轮发电机组振动故障诊断方法[J].电力系统保护与控制,2014,42(8):90-94.[点击复制] |
| LI Yong-de,LI Hong-wei,ZHANG Bing-cheng,et al.Fault diagnosis of gas turbine generator set by combination of rough sets and neural network[J].Power System Protection and Control,2014,42(8):90-94[点击复制] |
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| 摘要: |
| 针对燃气轮发电机组振动故障诊断中可测参数难以直接反映机组故障状态的问题，提出一种融合粗糙集理论和神经网络的燃气轮发电机组振动故障诊断方法。结合粗糙集对燃气轮发电机组振动信号原始特征数据进行约简，减少冗余信息。将粗糙集与神经网络有机结合，用优化了的神经网络诊断燃气轮发电机组振动故障。试验结果表明了所述方法的有效性，为燃气轮发电机组振动故障的快速诊断提供了可参考的新思路。 |
| 关键词: 燃气轮发电机组 故障诊断 粗糙集 神经网络 |
| DOI： |
| 投稿时间：2013-07-29修订日期：2013-11-11 |
| 基金项目: |
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| Fault diagnosis of gas turbine generator set by combination of rough sets and neural network |
| LI Yong-de,LI Hong-wei,ZHANG Bing-cheng,YANG Jie,LIU Hao-ying,ZHANG Jiao |
| (School of Electrical Engineering and Information, Southwest Petroleum University, Chengdu 610500, China;BKQ Production Plant, Petro China Xinjiang Oilfield Company, Karamay 834000, China) |
| Abstract: |
| In view of the problem that fault diagnosis for gas turbine vibration generator set parameters is difficult to reflect the state of unit fault directly, a fusion of rough set and neural network for gas turbine generator set vibration fault diagnosis is presented. Rough sets theory is applied in reduction of the original features of the vibration signal characteristic value data to remove unnecessary attributes. An optimized neural network structure which is used to fault diagnosis of gas turbine generator set is established based on rough sets. The experimental results show that the method is effective and provides a new idea for gas turbine generator set vibration fault diagnosis. |
| Key words: gas turbine generator set fault diagnosis rough set theory neural network |
