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首页> 外文期刊>Wireless personal communications: An Internaional Journal >Study on Prediction Methods for the Fault State of Rotating Machinery Based on Dynamic Grey Model and Metabolism Grey Model
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Study on Prediction Methods for the Fault State of Rotating Machinery Based on Dynamic Grey Model and Metabolism Grey Model

机译：基于动态灰色模型和新陈代谢灰色模型的旋转机械故障状态预测方法研究

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摘要

Rotors and bearings are the key parts of rotating machinery. Mechanical faults will occur easily when rotors and bearings are running for a long time in the condition of high speed and full load. In this paper, first the dynamic grey model and metabolism grey model (MGM) are respectively used to predict the trend of the vibration amplitude of rotors and bearings, and the prediction results are compared. Then based on the root mean square value of the vibration amplitude of rotors and bearings, a back propagation network prediction model of fault feature information is established, which can predict the fault of rotors and bearings in advance. Experiments show that the dynamic grey model can predict both the rising and comprehensive growth trends of the vibration signal amplitude of rotors and bearings. However, the prediction error will increase with an increase of vibration amplitude. Experiments also indicate that the accuracy of prediction based on the MGM is higher than that of dynamic grey model.
机译：转子和轴承是旋转机械的关键部件。当转子和轴承在高速和满载条件下运行长时间时，机械故障将很容易地发生。在本文中，首先，动态灰色模型和新陈代谢灰度模型（MGM）分别用于预测转子和轴承的振动幅度的趋势，并且比较预测结果。然后基于转子和轴承振动幅度的根均方值，建立了故障特征信息的背部传播网络预测模型，其可以预先预测转子和轴承的故障。实验表明，动态灰色模型可以预测转子和轴承的振动信号幅度的上升和综合生长趋势。然而，预测误差会随着振动幅度的增加而增加。实验还表明，基于MGM的预测精度高于动态灰色模型的准确性。

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