At present, the tool remaining useful life prediction technology is important to the effectiveness of machining, because tool life prediction plays the role of safety maintenance, cost optimization and quality assurance. However, this the technology faces many challenges in practical applications. The main problems include that when the spatial distribution of data features is too different, the model is difficult to adapt to multi-scene data and the feature extraction of data time series is not obvious. Therefore, this paper proposes a method for predicting the remaining useful life of milling cutters by using multi-scale spatial data feature visualization and domain separation prediction network (MTF-SE-DSPNs). Firstly, the one-dimensional time series data are globally normalized by this method, and then the processed data are transformed into images by MTF, which enhances the time series features expression ability of data. At the same time, the convolutional neural network based on DenseNet architecture is used and SElayer is added to adjust the feature weight to mine the sensitive information in the signal. To improve the prediction ability of the model, the time decay factor ξT is introduced to optimize the reconstruction loss, so that it can dynamically measure the relative importance of source domain and target domain data and improve the robustness of feature information reconstruction. Finally, the effectiveness of the method is validated by milling experiments under the same and different working conditions. The experimental results are compared with the other models, which proves the significant advantages of the model in various tasks.

中文翻译：

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一种基于多尺度空间数据特征可视化和域分离预测网络的铣刀剩余使用寿命预测方法


目前，刀具剩余使用寿命预测技术对加工的有效性很重要，因为刀具寿命预测起着安全维护、成本优化和质量保证的作用。然而，该技术在实际应用中面临许多挑战。主要问题包括当数据特征的空间分布差异太大时，模型难以适应多场景数据，数据时间序列的特征提取不明显。因此，本文提出了一种利用多尺度空间数据特征可视化和域分离预测网络 （MTF-SE-DSPNs） 预测铣刀剩余使用寿命的方法。首先，该方法对一维时间序列数据进行全局归一化，然后通过MTF将处理后的数据转化为图像，增强了数据的时间序列特征表达能力。同时，采用基于 DenseNet 架构的卷积神经网络，并加入 SElayer 调整特征权重，挖掘信号中的敏感信息。为了提高模型的预测能力，引入时间衰减因子 ξT 来优化重建损失，使其能够动态度量源域和目标域数据的相对重要性，提高特征信息重建的鲁棒性。最后，通过相同和不同工况下的铣削实验验证了该方法的有效性。将实验结果与其他模型进行比较，证明了该模型在各种任务中的显著优势。