基于自适应权重时间卷积网络的剩余使用寿命预测方法

Remaining useful life prediction based on an adaptive weight temporal convolutional network

随着人工智能和大数据技术的发展,基于深度学习的高端装备剩余使用寿命预测技术倍受关注,准确的剩余使用寿命预测对于高端装备安全运行意义重大。大多数基于深度学习的预测方法在构建主体预测结构通常利用循环神经网络、长短期记忆网络和门控单元等手段,但存在并行计算能力较差、预测精度不足的问题。针对上述问题,提出一种基于自适应权重时间卷积网络的寿命预测方法。采用时间卷积网络搭建预测模型,利用空洞因果卷积和残差连接结构增强并行计算能力并避免信息泄露;构建基于自注意力机制的自适应权重模块,实现时序权重自动分配来提高预测精度;利用非对称损失函数增强提前预测倾向性,避免因滞后预测带来的安全和经济问题。选取发动机数据集和轴承数据集进行实验验证,结果表明,与其他深度学习方法相比,提出的自适应权重时间卷积网络提升了预测准确率并缩短了训练时间。

With the development of artificial intelligence and big data,remaining useful life prediction technology based on deep learning for high-end equipment has attracted much attention.Accurate prediction of remaining useful life is a significant parameter affecting the safe operation of high-end equipment.Deep learning-based methods commonly use recurrent neural networks,and long and short-term memory networks and gating units when constructing the main prediction structure.However,there are problems of poor parallel computing capability and insufficient prediction accuracy.In order to address these problems,we propose a useful life prediction method based on adaptive weight temporal convolutional networks.The prediction model is built using a temporal convolutional network,and the parallel computing capability is enhanced and information leakage is avoided by using dilated causal convolution and a residual connection structure.An adaptive weight module based on a self-attention mechanism is constructed to realize the automatic assignment of temporal weights to improve the prediction accuracy.The asymmetric loss function is used to enhance the tendency of advance prediction and avoid the safety and economic problems caused by lagging prediction.Engine datasets and bearing datasets were selected for experimental validation.The results showed that the proposed adaptive weight temporal convolutional network improved the prediction accuracy and reduced the training time compared with other deep learning methods.