Health prediction of rolling bearing based on digital twin
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摘要: 滚动轴承的健康状态在很大程度上决定了机械设备的可靠性水平。对滚动轴承进行健康状态预测有助于机械设备的安全性运行。因此，提出了一种基于数字孪生的滚动轴承剩余寿命实时预测方法。首先，该方法基于数字孪生的数字化技术手段获取滚动轴承的实时感知信息，从而建立考虑实时工况变化的滚动轴承数字孪生模型。其次，通过非线性布朗运动建立考虑测量误差的剩余寿命预测模型。然后，采用极大似然估计方法求解模型中的未知参数，并利用贝叶斯理论实时更新参数，从而对滚动轴承的剩余寿命进行实时预测。最后，通过滚动轴承的全寿命周期信息分析验证了该方法的可行性和有效性。Abstract: The health status of rolling bearings largely determines the reliability level of mechanical equipment. The health state prediction of rolling bearings can help the safe operation of mechanical equipment. Therefore, this paper proposes a real-time prediction method of the remaining life of rolling bearings based on digital twin. Firstly, the method is based on digital twin technology means to obtain real-time sensing information of rolling bearings, so as to establish a digital twin model of rolling bearings considering real-time working condition changes. Secondly, a remaining life prediction model considering the measurement error is established by non-linear Brownian motion. Then, the unknown parameters in the model are solved by using the great likelihood estimation method, and the parameters are updated in real time by using Bayesian theory, so that the remaining life of the rolling bearing can be predicted in real time. Finally, the feasibility and effectiveness of the method are verified by analyzing the information of the whole life cycle of rolling bearings.
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Key words:
- digital twin /
- remaining life prediction /
- rolling bearing /
- brownian motion
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