基于Autoformer的滚动轴承剩余使用寿命预测

滚动轴承剩余使用寿命(RUL)预测的数据驱动方法显示出了巨大的潜力,但仍有提升的空间。为此,提出了一种基于Autoformer模型的滚动轴承RUL预测方法。结合领域内的专家知识对滚动轴承原始信号进行人工特征提取并优化特征,利用Transformer类模型强大的多维特征提取能力挖掘输入特征与RUL之间的复杂映射关系。针对滚动轴承振动信号的周期性特点采用Autoformer模型将时间序列进行分解对趋势项和周期项分别处理。实验结果表明,所提出的预测方法在PHM2012数据集上的表现相比于其它文献的方法,平均得分分别提高了50.03%、21.31%、19.93%。证明了该方法的优越性。

Remaining useful life prediction of rolling bearing based on Autoformer

The data-driven approach to rolling bearing remaining useful life (RUL) prediction shows great potential, but there is still room for improvement. Therefore, a prediction method of rolling bearing RUL based on Autoformer model is proposed. Combined with the expert knowledge in this field, the original signal of rolling bearing is artificially extracted and optimized, and the complex mapping relationship between input features and RUL is mined by using the powerful multi-dimensional feature extraction capability of Transformer models. According to the periodic characteristics of the vibration signal of rolling bearings, the Autoformer model is used to decompose the time series to deal with the trend term and the periodic term separately. Experimental results show that the average scores of the proposed prediction method on the PHM2012 dataset is improved by 50.03%, 21.31% and 19.93% respectively, compared with other methods in the literature. Proves the superiority of this method.