Turbine Fault Diagnosis Based on Fuzzy Theory and SVM

机译：基于模糊理论和支持向量机的汽轮机故障诊断

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A method based on fuzzy and support vector machine (SVM) is proposed to focus on the lack of samples in fault diagnosis of turbine. Typical fault symptoms firstly are normalized by the membership functions perceptively. Then some samples are used to train SVM of fault diagnosis. With the trained SVM, the correct fault type can be recognized. In the application of condenser fault diagnosis, the approach enhances successfully the accuracy of fault diagnosis with small samples. Compared with the general method of BP neural network, the method combining advantages of fuzzy theory and SVM makes the diagnosis results have higher credibility.

机译：针对汽轮机故障诊断中样本量少的问题，提出了一种基于模糊支持向量机的方法。典型的故障症状首先通过隶属函数在感知上进行标准化。然后使用一些样本来训练故障诊断的支持向量机。使用训练有素的SVM，可以识别正确的故障类型。在冷凝器故障诊断中的应用，该方法成功地提高了小样本故障诊断的准确性。与常规的BP神经网络方法相比，该方法结合了模糊理论和支持向量机的优势，使得诊断结果具有较高的可信度。

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来源
《Artificial intelligence and computational intelligence》|2009年|P.668-676|共9页
会议地点 Shanghai(CN);Shanghai(CN)
作者
Fei Xia; Hao Zhang; Daogang Peng; Hui Li; Yikang Su;
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作者单位

College of Electric Power and Automation Engineering,Shanghai University of Electric Power,200090 Shanghai, China;

College of Electric Power and Automation Engineering,Shanghai University of Electric Power,200090 Shanghai, China;

College of Electric Power and Automation Engineering,Shanghai University of Electric Power,200090 Shanghai, China;

College of Electric Power and Automation Engineering,Shanghai University of Electric Power,200090 Shanghai, China;

Nanchang Power Supply Corporation,330006 Nanchang, JiangXi, China;

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原文格式 PDF
正文语种 eng
中图分类人工智能理论;
关键词
turbine fault diagnosis; fuzzy theory; SVM;

机译：涡轮故障诊断；模糊理论支持向量机;

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专利

1. Fault detection and diagnosis of an industrial steam turbine using fusion of SVM (support vector machine) and ANFIS (adaptive neuro-fuzzy inference system) classifiers [J] . Karim Salahshoor, Mojtaba Kordestani, Majid S. Khoshro Energy . 2010,第12期

机译：支持向量机和支持神经模糊推理系统分类器融合的工业汽轮机故障检测与诊断
2. Wind turbine fault diagnosis method based on diagonal spectrum and clustering binary tree SVM [J] . Liu Wenyi, Wang Zhenfeng, Han Jiguang, Renewable energy . 2013,第FEBa期

机译：基于对角谱和聚类二叉树支持向量机的风机故障诊断方法
3. Fuzzy model-based faults diagnosis of the wind turbine benchmark [J] . Ayoub EL Bakri, Ismail Boumhidi Procedia Computer Science . 2018,第5期

机译：基于模糊模型的风力涡轮机基准的故障诊断
4. Turbine Fault Diagnosis Based on Fuzzy Theory and SVM [C] . Fei Xia, Hao Zhang, Daogang Peng, International Conference on Artificial Intelligence and Computational Intelligence . 2009

机译：基于模糊理论和SVM的涡轮机故障诊断
5. A new methodology based on fuzzy set theory and fault tree analysis for failure diagnosis in nuclear power plants. [D] . Abdelhai, Mohamed Ibrahim. 1993

机译：基于模糊集理论和故障树分析的核电站故障诊断新方法。
6. An SVM-Based Solution for Fault Detection in Wind Turbines [O] . Pedro Santos, Luisa F. Villa, Aníbal Reñones, 2015

机译：基于SVM的风力发电机组故障检测解决方案
7. Diagnosis and Early Warning of Wind Turbine Faults Based on Cluster Analysis Theory and Modified ANFIS [O] . Quan Zhou, Taotao Xiong, Mubin Wang, 2017

机译：基于聚类分析理论和改进aNFIs的风力机故障诊断与预警

Turbine Fault Diagnosis Based on Fuzzy Theory and SVM

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