COMPILED: Deep Metric Learning for Defect Classification of Threaded Pipe Connections using Multichannel Partially Observed Functional Data
This work addresses defect type identification for root cause diagnosis in manufacturing, but it is incremental as it applies deep metric learning to a specific domain problem.
The paper tackles defect classification in threaded pipe connections using imbalanced, multichannel, and partially observed functional data, proposing the COMPILED deep metric learning approach, which demonstrates superior accuracy in a real-world case study.
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.