In modern manufacturing, most of the product lines are conforming. Few products are nonconforming but with different defect types. The identification of defect types can help further root cause diagnosis of production lines. With the sensing development, signals of process variables can be collected in high resolution, which can be regarded as multichannel functional data. They have abundant information to characterize the process and help identify the defect types. Motivated by a real example from the pipe tightening process, we focus on defect classification where each sample is a multichannel functional data. However, the available samples for each defect type are limited and imbalanced. Moreover, the functions are incomplete since the pre-tightening process before the pipe tightening process is unobserved. To classify the defect samples based on imbalanced, multichannel, and incomplete functional data is very important but challenging. Thus, we propose an innovative classification framework based on deep metric learning using functional data (DeepFunction). The framework leverages the power of deep metric learning to train on imbalanced datasets. A neural network specially crafted for processing functional data is also proposed to handle multichannel and incomplete functional data. The results from a real-world case study demonstrate the superior accuracy of our framework when compared to existing benchmarks.

翻译：在现代制造业中，绝大多数产品线均符合标准，仅有少数产品存在缺陷，且缺陷类型各异。缺陷类型的识别有助于进一步追溯生产线的根本原因。随着传感技术的发展，过程变量的信号可被高分辨率采集，这些信号可视为多通道功能数据，蕴含丰富的工艺特征信息，可用于缺陷类型的识别。受管道拧紧工艺实际案例的启发，本研究聚焦于以多通道功能数据为样本的缺陷分类问题。然而，各类缺陷的可用样本数量有限且分布不均衡，加之管道拧紧前的预紧过程无法观测导致功能数据不完整，使得基于不平衡、多通道及不完整功能数据的缺陷分类至关重要但极具挑战性。为此，我们提出一种基于深度度量学习的功能数据分类新框架（DeepFunction）。该框架利用深度度量学习的优势对不平衡数据集进行训练，同时专门设计了处理功能数据的神经网络以应对多通道及不完整功能数据。实际案例研究结果表明，与现有基准方法相比，本框架具有更优的分类精度。