旋转机械故障诊断的灰度-基元共生矩阵方法研究

窦唯; 刘占生; 马晓峰

旋转机械故障诊断的灰度-基元共生矩阵方法研究

哈尔滨工业大学能源科学与工程学院, 哈尔滨 150001

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出版历程
- 收稿日期: 2007-09-15
- 修回日期: 2007-12-20
- 刊出日期: 2008-09-28

Fault diagnosis of rotating machinery based on gray primitive co-occurrence matrix

School of Energy Science and Engineering, Harbin Institute of Technology, Harbin 150001, China

摘要

摘要: 研究了基于灰度-基元共生矩阵的故障诊断方法,该方法利用图形的灰度空间分布情况和描述纹理的纹理基元法结合起来提取和挖掘旋转机械振动状态参数图形特征,可以有效地提取图形中纹理特征信息,直接利用BP人工神经网络实现旋转机械故障诊断。在600MW模化汽轮机转子试验台上进行了转子正常、转子不平衡故障、转子不对中故障及轴承松动故障的试验,诊断结果表明可以获得较高的诊断精度.
- 旋转机械 /
- 故障诊断 /
- 灰度-基元共生矩阵 /
- 参数图形 /
- 纹理特征
Abstract: Afault diagnosis method based on gray primitive co-occurrence matrix was analyzed.This method directly extracts the image characteristics about the vibration parameter of rotating machinery by combining ray space distribution characteristics with texture primitive method.While extracting the information of image texture characteristic,it enables fault diagnosis of rotating machinery by using BP(back propagation) artificial neural networks.This method was validated to get high diagnosis accuracy by conducting normal state rotor,unbalanced rotor,misalignment rotor and loose bearing pedestal experiments on　600 MWturbine experimental bench.
- rotating machinery /
- fault diagnosis /
- gray primitive co-occurrence matrix /
- parameters image /
- texture feature

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参考文献(13)

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