Domain Adversarial Transfer Learning for Generalized Tool Wear Prediction
Domain Adversarial Transfer Learning for Generalized Tool Wear Prediction
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DOI：
10.36001/phmconf.2020.v12i1.1137
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发表时间：
2020-11
期刊：
影响因子：
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通讯作者：
Peng Wang;Matthew Russell
中科院分区：
文献类型：
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作者：
Peng Wang;Matthew Russell
鉴于其在分析和揭示数据背后的模式方面的能力，深度学习(DL)已被越来越多地研究，以在智能制造的各个方面补充基于物理的模型，如机器状态监测和故障诊断、复杂制造过程建模和质量检测。然而，数据挖掘技术的成功实施在很大程度上依赖于用于稳健网络训练的数据的数量、种类和准确性。此外，用于网络训练和应用的数据的分布应该相同，以避免降低网络性能适用性的内部协方差漂移问题。作为应对这些挑战的一种有前途的解决方案，转移学习(TL)使在源域和任务上训练的DL网络能够应用于单独的目标域和任务。本文基于产生式对抗性网络的概念，提出了一种领域对抗性翻译方法。在该方法中，优化器寻求最小化来自源域的已标记训练样本的损失(即，回归或分类精度)，同时最大化源和目标数据集上的域分类器的损失(即，最大化源和目标特征的相似性)。所开发的领域对抗性TL方法已在一维CNN主干网络上实现，并使用NASA的铣削数据集对刀具磨损传播进行了评估。实验结果表明，领域对抗性TL能够成功地将针对特定场景训练的DL模型应用于新的目标任务。
Given its demonstrated ability in analyzing and revealing patterns underlying data, Deep Learning (DL) has been increasingly investigated to complement physics-based models in various aspects of smart manufacturing, such as machine condition monitoring and fault diagnosis, complex manufacturing process modeling, and quality inspection. However, successful implementation of DL techniques relies greatly on the amount, variety, and veracity of data for robust network training. Also, the distributions of data used for network training and application should be identical to avoid the internal covariance shift problem that reduces the network performance applicability. As a promising solution to address these challenges, Transfer Learning (TL) enables DL networks trained on a source domain and task to be applied to a separate target domain and task. This paper presents a domain adversarial TL approach, based upon the concepts of generative adversarial networks. In this method, the optimizer seeks to minimize the loss (i.e., regression or classification accuracy) across the labeled training examples from the source domain while maximizing the loss of the domain classifier across the source and target data sets (i.e., maximizing the similarity of source and target features). The developed domain adversarial TL method has been implemented on a 1-D CNN backbone network and evaluated for prediction of tool wear propagation, using NASA's milling dataset. Performance has been compared to other TL techniques, and the results indicate that domain adversarial TL can successfully allow DL models trained on certain scenarios to be applied to new target tasks.