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A cost-effective manufacturing process recognition approach based on deep transfer learning for CPS enabled shop-floor
Robotics and Computer-Integrated Manufacturing ( IF 10.4 ) Pub Date : 2021-01-30 , DOI: 10.1016/j.rcim.2021.102128
Bufan Liu , Yingfeng Zhang , Jingxiang Lv , Arfan Majeed , Chun-Hsien Chen , Dang Zhang

The rapid development of the industrial Internet of Things has promoted manufacturing to develop towards the cyber-physical system, of which highly accurate process recognition plays an important role in achieving proactive monitoring of intelligent manufacturing process. Compared to the traditional handcrafted feature-based method, deep model owns convenience in terms of extracting feature automatically for the recognition. However, training a deep model is time-consuming and also requires large-scale training samples. To solve these problems and obtain high accuracy in the meanwhile, a deep transfer learning-based manufacturing process recognition approach is proposed in this study. A pre-trained model based on a convolutional neural network is used to extract low dimensional features followed by a fine-tuning process to target the specific process recognition task. Experimental verification of two datasets was conducted to demonstrate this cost-effective method. The results showed the proposed method can get better accuracy with less training time and fewer training samples.



中文翻译:

基于深度转移学习的具有成本效益的制造过程识别方法,用于启用CPS的车间

工业物联网的快速发展推动了制造业向网络物理系统的发展,其中高度精确的过程识别在实现对智能制造过程的主动监控中起着重要作用。与传统的手工基于特征的方法相比,深度模型在自动提取特征以进行识别方面具有便利性。但是,训练深度模型非常耗时,并且还需要大规模的训练样本。为了解决这些问题并同时获得较高的精度,本研究提出了一种基于深度转移学习的制造过程识别方法。基于卷积神经网络的预训练模型用于提取低维特征,然后进行微调以针对特定的过程识别任务。对两个数据集进行了实验验证,以证明该成本有效的方法。结果表明,该方法在减少训练时间,减少训练样本的情况下,具有较高的准确性。

更新日期:2021-01-31
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