Research on the Order Selection of the Autoregressive Modelling for Rolling Bearing Diagnosis
Research on the Order Selection of the Autoregressive Modelling for Rolling Bearing Diagnosis
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DOI：
10.1243/09544062jmes1958
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发表时间：
2010-10
期刊：
影响因子：
--
通讯作者：
F. Cong;J. Chen;G. Dong
中科院分区：
文献类型：
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作者：
F. Cong;J. Chen;G. Dong
对滚动轴承自回归模型的阶次选择进行了研究。首先，考虑与McFadden介绍的信号相匹配的信号模型。为了清楚地描述它，这里称之为共振阻尼模型。结果表明，故障产生的冲击将引起结构共振，并很快以周期模式衰减。由于基于预测理论的AR过程具有识别周期（准周期）部分的能力，因此可以识别出准周期分量的阻尼部分。由于背景噪声的存在，如果衰减分量完全被噪声掩埋，则无法识别阻尼部分。因此，最佳阶数应该是过程所包含的点的数量，这是AR模型可以识别的周期阻尼部分的最大长度。也就是说，这个过程应该持续到谐振阻尼部分完全被掩埋在噪声中。通过实验对该方法进行了验证，并在真实的滚动轴承故障诊断中取得了成功。最后，得出的结论是，最佳顺序有很高的能力，消除噪声的滚动轴承诊断。
The article does a research on the order selection of the autoregressive (AR) model for the rolling element bearings. First, the model of the signal that matches the one introduced by McFadden is considered. To clearly describe it, here it is called the resonance damping model. It is shown that the impulses generated by a fault will cause structure resonance and soon decay with a periodic mode. As the AR process based on the prediction theory has an ability to recognize the periodic (quasi-periodic) part, it is possible to pick out the damping part that is a quasi-periodic component. Because of the background noise, the damping part cannot be recognized if the component decays to be buried into noise absolutely. Hence the optimal order should be the number of points contained by the process, which is the maximum length of the periodic damping part that the AR model can recognize. That is to say, the process should last until the resonance damping part is buried into noise completely. Then an experiment to validate the method is carried out and success is achieved in the fault diagnosis of real rolling bearings. In the end, it is concluded that the optimal order has a high ability for noise cancellation for rolling element bearing diagnosis.