气动准零非线性隔振器的刚度特性与参数调控

为解决滚动轴承剩余寿命预测问题，提出一种基于长短期记忆网络（long short-term memory, 简称LSTM）的剩余寿命预测方法。首先，从时域、频域及时频域特征中提取特征参数；其次，定义三个评价指标定量评估表征轴承退化过程的特征参数效果，筛选得到退化特征参数集，搭建长短期记忆网络预测模型并以归一化寿命值为标签训练神经网络；最后，用训练好的神经网络实现滚动轴承剩余寿命预测。通过滚动轴承全寿命试验证明，该方法可以准确预测滚动轴承剩余寿命，并与反向传播(back propagation machine,简称BP)神经网络和支持向量回归机（support vector regression machine，简称SVRM）的预测效果对比，验证了提出方法的有效性。

Stiffness Properties and Tuning Analysis for the Smooth Type Pneumatic Quasi-zero Vibration Isolator

In light of prediction of remaining useful life (RUL) of rolling bearings, a long short-term memory (LSTM) network is introduced into traditional methods. First, the feature of rolling bearings is extracted from the time domain, the frequency domain and the time-frequency domain. Then, three evaluation indexes are defined to characterize the degeneration, and the data is filtered for a degenerated feature set to train the LSTM network prediction model. Finally, the remaining useful life is predicted by the trained neural network. The proposed method is accurate in prediction and superior to back propagation (BP) neural network and support vector regression