2024/04/04 by Juan Du, Du, Juan, Yukun Xie +3
Computer Science · Engineering · #FOS: Computer and information sciences #FOS: Electrical engineering #Imbalanced Data Classification Techniques #Industrial Vision Systems and Defect Detection #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Non-Destructive Testing Techniques #Signal Processing (eess.SP) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2404.03329
openalex publication_date 2024/04/04 · openalex created_date 2024/04/06 · openalex updated_date 2026/07/28
In modern manufacturing, most products are conforming. Few products are nonconforming with different defect types. The identification of defect types can help further root cause diagnosis of production lines. With the sensing technology development, process variables evolved as time changes, which can be collected in high resolution as multichannel functional data. These functional data have rich information to characterize the process and help identify the defect types. Motivated by a real example from the threaded pipe connection process, we focus on defect classification where each sample is represented as partially observed multichannel functional data. However, the available samples for each defect type are limited and imbalanced. The functional data is partially observed since the pre-connection process before the threaded pipe connection process is unobserved as there is no sensor installed in the production line. Therefore, the defect classification based on imbalanced, multichannel, and partially observed functional data is very important but challenging. To deal with these challenges, we propose an innovative classification approach named as COMPILED based on deep metric learning. The framework leverages the power of deep metric learning to train on imbalanced datasets. A novel neural network structure is proposed to handle multichannel partially observed functional data. The results from a real-world case study demonstrate the superior accuracy of our framework when compared to existing benchmarks.