基于多通道融合的滚动轴承剩余寿命预测

针对工业生产中滚动轴承剩余使用寿命(RUL)预测任务中数据挖掘不足导致预测精度低的问题，提出了一种多通道融合的滚动轴承剩余寿命预测方法。该方法通过互补集合经验模态分解(CEEMD)对原始振动信号进行降噪化处理和特征增强并将其作为模型输入；构建三通道网络模型，引入3种不同的神经网络：时间卷积网络(TCN)、卷积长短时间记忆网络(ConvLSTM)、双向门控循环单元神经网络(Bi-GRU),从时序、空间、感受野等多维度对特征进行差异化提取；在结构基础上添加多头注意力机制(multi-head attention mechanism, MA),重新调整网络输出权重、加快模型收敛速度；最后，设计一个特征融合输出模块，实现对滚动轴承剩余寿命预测。在两种数据集上进行实验验证，并与其他文献中先进模型进行对比。结果表明，所提模型能够更准确地捕捉轴承寿命退化曲线并且在多种评价指标上均优于对比模型。

Residual life prediction of rolling bearings based on multi-feature fusion

In order to solve the problem of low prediction accuracy caused by insufficient data mining in the prediction task of remaining useful life (RUL) of rolling bearings in industrial production, a multi-channel fusion method for predicting the remaining life of rolling bearings was proposed. In this method, the original vibration signal is denoised and feature enhanced by complementary ensemble empirical mode decomposition (CEEMD) is taken as input. A three-channel network model was constructed, and three different neural networks were introduced: Temporal convolutional networks (TCN), convolutional long short-term memory network (ConvLSTM), and bidirectional gated recurrent unit neural network (Bi-GRU), which differentially extracts features from multiple dimensions such as time series, space, and receptive field. The multi-head attention mechanism (MA) is added on the basis of the structure to readjust the output weight of the network and accelerate the convergence speed of the model. Finally, a feature fusion output module was designed to predict the remaining life of rolling bearings. Experimental verification was carried out on two datasets and compared with the advanced models in other literatures. The results show that the proposed model can capture the bearing life degradation curve more accurately, and is better than the comparison model in a variety of evaluation indicators.