Abstract：
Industrial data scarcity is one of the largest factors holding back the widespread use of machine learning in manufacturing. To overcome this problem, the concept of transfer learning was developed and has received much attention in recent industrial research. This paper focuses on the problem of time series segmentation and presents the first in-depth research on transfer learning for deep learning-based time series segmentation on the industrial use case of end-of-line pump testing. In particular, we investigate whether the performance of deep learning models can be increased by pretraining the network with data from other domains. Three different scenarios are analyzed: source and target data being closely related, source and target data being distantly related, and source and target data being non-related. The results demonstrate that transfer learning can enhance the performance of time series segmentation models with respect to accuracy and training speed. The benefit can be most clearly seen in scenarios where source and training data are closely related and the number of target training data samples is lowest. However, in the scenario of non-related datasets, cases of negative transfer learning were observed as well. Thus, the research emphasizes the potential, but also the challenges, of industrial transfer learning.
摘要：
工业数据稀缺是阻碍机器学习在制造业中广泛使用的最大因素之一。为了克服这个问题，迁移学习的概念得到了发展，并在最近的工业研究中受到了广泛的关注。本文重点研究了时间序列分割问题，并在末端泵测试的工业用例上首次对基于深度学习的时间序列分割进行了深入研究。特别是,我们研究是否可以通过使用来自其他领域的数据对网络进行预训练来提高深度学习模型的性能。分析了三种不同的情况：源数据和目标数据密切相关，源数据和目标数据密切相关，以及源数据和目标数据不相关。结果表明，迁移学习可以提高时间序列分割模型的准确性和训练速度。在源数据和训练数据密切相关且目标训练数据样本数量最低的情况下，可以最清楚地看到好处。然而,在非相关数据集的场景中，也观察到负迁移学习的情况。因此，这项研究强调了潜力，还有挑战，产业转移学习。
