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ORBIT SHAPE AUTOMATIC RECOGNITION BASED ON ARTIFICIAL NEURAL NETWORK

机译:基于人工神经网络的轨道形状自动识别

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Orbit is a significant symptom in the fault diagnosis of rotating machine. The orbit is a 2-D image and can be described by moment invariants, the shape property of 2-D image, which is a description with translating-, rotating-, and scaling-invariants for 2-D image. The descriptive method of orbit image is investigated and an automatic orbit shape recognition based on artificial neural network (ANN) with moment invariants is proposed in this paper. The ANN of orbit shape recognition is trained by the training patterns generated by computer simulation for plenty of orbit shapes. It is shown that the trained ANN is of good recognition performance and generalization capability when applied to recognition of the measured orbits. This method can be used to the intelligent expert system of fault diagnosis to obtain automatically online orbit symptom in shafts vibration monitoring of turbine generator, which will improve the automatization of obtaining fault symptom and the automatic diagnosis in the expert system.
机译:轨道是旋转机械故障诊断中的重要症状。轨道是2D图像,可以用矩不变性(2-D图像的形状属性)描述,这是对2-D图像具有平移,旋转和缩放不变性的描述。研究了轨道图像的描述方法,提出了一种基于带有不变矩的人工神经网络(ANN)的自动轨道形状识别方法。轨道形状识别的ANN由计算机模拟生成的大量轨道形状的训练模式进行训练。结果表明,训练后的人工神经网络在应用于实测轨道识别时具有良好的识别性能和泛化能力。该方法可用于故障诊断的智能专家系统中,以自动获取涡轮发电机轴振动监测中的在线轨道症状,从而提高专家系统中故障症状的自动获取和自动诊断。

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