Rubber bearings are a key component of base-isolated structures. Hence, it is important to effectively monitor the axial pressure in rubber bearings. A smart rubber bearing (SRB) and a wavelet-packet-based axial pressure index were introduced in the previous study. However, the wavelet-packet-based axial pressure index for evaluating the axial pressure in the SRB has notable shortcomings, because the system is susceptible to disturbances such as the change of sensitive subsets, generator-sensor couple and SRB specimen. This paper proposes the discrete wavelet packet transform enhanced bidirectional long-short term memory (DWPT-BiLSTM) method to evaluate the axial pressure state of the SRB. Three full-scale SRBs were tested, and two data augmentation methods were used to increase the dataset capacity and ensure the proper distribution of different axial pressure states in the dataset. By simultaneously considering the validation accuracy and training time, the optimal network structure is one hidden layer with 50 cells, and the optimal learning rate is 0.01. With these hyperparameters, the DWPT-BiLSTM method achieved an accuracy of 99.4% on the training data and an accuracy of 97.3% on the validation data in the evaluation of the axial pressure state of the SRB. The precision and recall of the axial pressure states were above 95% for both the training and validation set.

橡胶支座是基础隔震结构的关键部件。因此，有效监测橡胶轴承中的轴向压力非常重要。在之前的研究中引入了智能橡胶轴承（SRB）和基于小波包的轴向压力指数。然而，基于小波包的轴压指数评估SRB轴压具有明显的缺点，因为系统容易受到敏感子集、发电机-传感器耦合和SRB样本变化等干扰。本文提出了离散小波包变换增强双向长短期记忆（DWPT-BiLSTM）方法来评估SRB的轴向压力状态。测试了三个全尺寸 SRB，并使用两种数据增强方法来增加数据集容量并确保数据集中不同轴向压力状态的正确分布。同时考虑验证精度和训练时间，最优网络结构为1个隐层50个单元，最优学习率为0.01。有了这些超参数，DWPT-BiLSTM 方法在 SRB 轴向压力状态的评估中，在训练数据上达到了 99.4% 的准确率，在验证数据上达到了 97.3% 的准确率。对于训练集和验证集，轴向压力状态的准确率和召回率均在 95% 以上。最佳学习率为0.01。有了这些超参数，DWPT-BiLSTM 方法在 SRB 轴向压力状态的评估中，在训练数据上达到了 99.4% 的准确率，在验证数据上达到了 97.3% 的准确率。对于训练集和验证集，轴向压力状态的准确率和召回率均在 95% 以上。最佳学习率为0.01。有了这些超参数，DWPT-BiLSTM 方法在 SRB 轴向压力状态的评估中，在训练数据上达到了 99.4% 的准确率，在验证数据上达到了 97.3% 的准确率。对于训练集和验证集，轴向压力状态的准确率和召回率均在 95% 以上。