基于策略梯度Informer模型的滚动轴承剩余寿命预测

Remaining Useful Life Prediction of Rolling Bearings Based on Policy Gradient Informer Model

典型的编码器-解码器——Transformer存在二次时间复杂度、高内存使用及模型结构复杂等固有限制,造成Transformer用于滚动轴承剩余寿命(RUL)预测会表现出较低预测精度和较低计算效率的问题。为此,提出一种新型编解码器——策略梯度Informer(PG-Informer)模型,并将其应用于滚动轴承RUL预测。首先,在PG-In-former的新型编解码器体系结构——Informer中设计了概率稀疏自注意力(PSSA)机制替代Transformer中原有的自注意力机制,以提高非线性逼近能力并减少时间和空间复杂度;然后,PG-Informer采用自注意力蒸馏(SAD)操作减少参数维度和参数量,并提高了对时间序列的预测鲁棒性;此外,PG-Informer的生成式解码器对解码输入进行一步解码输出预测结果,无需动态多步解码,提升了对时间序列的预测速度;最后,构造了策略梯度学习算法来提高对PG-Informer参数的训练速度。PG-Informer的以上优势使所提出的基于PG-Informer模型的滚动轴承RUL预测方法可以获得较高的预测精度、较好的鲁棒性和较高的计算效率。对辛辛那提大学智能维护系统中心的1号滚动轴承的RUL预测实验结果表明,所提出方法预测得到的RUL值为963min,其RUL预测误差仅为6.50%,比基于Transformer的RUL预测方法预测精度更高、预测误差更小、鲁棒性更好;所提出方法所耗费的RUL预测时间仅为132.37s,比基于Transformer的RUL预测方法的预测时间更短。以上实验结果验证了所提出方法的有效性。

As a typical encoder-decoder,the transformer architecture has inherent limitations such as secondary time complexity,high memory usage,and a complex model structure;these issues can lead to lower prediction accuracy and decreased computational efficiency when applied to the prediction of the remaining useful life(RUL)of rolling bearings.For this reason,herein,a novel encoder-decoder,the Policy Gradient Informer(PG-Informer)model,is proposed and applied to the prediction of the RUL of rolling bearings for the first time.First,in the new encoder-decoder architecture of PG-Informer,a probabilistic sparse self-attention m echanism is used to replace the original self-attention mechanism of the transformer architecture to improve its nonlinear approximation ability and reduce its time and space complexity.Then,the self-attention distillation operation is used to reduce its number of parameters and their dimensions and enhance the prediction robustness of time series.Moreover,the generative decoder of PG-Informer only needs to decode the decoding input in one step to output the prediction results without dynamic multistep decoding,which improves the prediction speed of time series.Finally,a policy-gradient learning algorithm is constructed to improve the training speed of the PG-Informer parameters.These advantages enable the proposed rolling-bearing RUL-prediction method to obtain higher prediction accuracy,better robustness,and higher computational efficiency.Results for the No.1 rolling bearing at the Center for Intelligent Maintenance Systems of the University of Cincinnati show that the proposed method was able to predict an RUL value of 963 min,representing a prediction error of only 6.50% when compared to the experimental result;compared to the transformer-based RUL prediction method,this represents a higher prediction accuracy,a smaller prediction error,and greater robustness.The proposed method consumed only 132.37 s for RUL prediction,shorter than the time taken by the transformer-based RUL-prediction method.These results verify the effectiveness and advantages of the proposed method.