采用TCN-HS的滚动轴承剩余使用寿命预测

Prediction of remaining useful life of rolling bearingsby using TCN-HS

滚动轴承作为旋转机械中的关键部件,对其剩余使用寿命RUL(remained useful life)的准确预测可以帮助维修人员及时制定维修计划,延长设备工作时间,保证安全。由于利用数学建模精确建立轴承退化过程的模型涉及到复杂的物理过程,所以以深度学习为基础的基于数据驱动的方法已经成为主流方法。提出了一种融合混合膨胀卷积与自适应斜率软阈值函数的时间卷积神经网络TCN-HS(temporal convolutional network with hybrid dilated convolution and self-adaptive slope thresholding)用于滚动轴承寿命预测。模型使用混合膨胀卷积HDC(hybrid dilated convolution)解决了栅格效应问题,并利用自适应斜率软阈值函数(self-adaptive slope thresholding)进一步筛选特征。为了验证TCN-HS模型的有效性,基于PHM2012轴承数据集进行了实验,结果表明:改进方法提升了模型的性能,预测结果准确。

A rolling bearing is a key component of a rotating machinery,and the accurate prediction of its remaining useful life(RUL)can help maintenance personnel make maintenance plans in time,prolong equipment working time and ensure safety.Because it involves complex physical process to accurately establish a model of bearing degradation process through mathematical modeling,the data-driven method based on deep learning becomes a popular alternative method.This paper proposes an improved temporal convolutional network with hybrid dilated convolution and self-adaptive slope thresholding(TCN-HS)function to predict the RUL of rolling bearings.This model uses hybrid dilated convolution(HDC)to solve the problem of grid effect,and uses self-adaptive slope thresholding functions to further screen features.In order to verify the effectiveness of TCN-HS model,experiments are carried out based on PHM2012 bearing data set.The results show that the improved method upgrades the model and the prediction results are accurate.