Deep metric learning for defect classification of threaded pipe connections using multichannel partially observed functional data

In manufacturing systems, the identification of defect types can help further root cause diagnosis of production lines. With the development of sensing technology, the time-evolving process variables can be collected in high resolution as multichannel functional data. These functional data contain rich information for characterizing the process and identifying 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 no sensor is installed in the pre-connection process, which is an upstreaming stage before the threaded pipe connection process. Therefore, the defect classification based on imbalanced, multichannel, and partially observed functional data is necessary while challenging. To deal with these challenges, we propose an innovative classification approach based on deep metric learning. 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.