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首页> 外文期刊>Structural health monitoring >Remaining useful life prediction of rolling element bearings using degradation feature based on amplitude decrease at specific frequencies
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Remaining useful life prediction of rolling element bearings using degradation feature based on amplitude decrease at specific frequencies

机译：使用退化特征基于特定频率下振幅降低的滚动轴承的剩余使用寿命预测

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This research presents a new method of degradation feature extraction to predict remaining useful life, the remaining time to the maintenance, of rolling element bearings. Since bearing fault is the foremost cause of failure in rotating machinery, there are many studies for evaluating bearings' health status to prevent a catastrophic failure. Most of these studies are based on health monitoring data, such as vibration signals that are indirectly related to bearing fault, from which degradation feature can be extracted. It is, however, challenging to extract a degradation feature that can be applied to all rolling elements. This study focuses on the amplitude decrease at specific frequencies, from which a robust degradation feature is extracted by employing the information entropy. Some important attributes are found from the degradation feature, which is used to predict the remaining useful life of bearings. This method is demonstrated using the real test data provided by FEMTO-ST Institute. The results show that bearings can be used up to 87% of their whole life and 59%-74% of life in average.
机译：这项研究提出了一种新的退化特征提取方法，以预测滚动轴承的剩余使用寿命和维护时间。由于轴承故障是旋转机械故障的最主要原因，因此有许多研究评估轴承的健康状况以防止发生灾难性故障。这些研究大多基于健康监测数据，例如与轴承故障间接相关的振动信号，可以从中提取出劣化特征。然而，提取可应用于所有滚动元件的退化特征是具有挑战性的。这项研究的重点是特定频率下的幅度下降，通过使用信息熵从中提取出鲁棒的降级特征。从退化特征中发现一些重要的属性，该属性用于预测轴承的剩余使用寿命。使用FEMTO-ST研究所提供的真实测试数据演示了此方法。结果表明，轴承的使用寿命可达其平均使用寿命的87％，平均使用寿命为59％-74％。

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