Pairwise Critical Point Detection Using Torque Signals in Threaded Pipe Connection Processes

Abstract

The quality of threaded pipe connections is a key characteristic in oil and gas industries. This paper introduces a novel method for detecting pairwise critical points in torque signals using a three-phase state-space model and a two-stage recursive particle filter, significantly enhancing detection accuracy and reliability in pipe connection processes.

Juan Du

Assistant Professor

My research interests include knowledge-infused data science for quality improvement, industrial data analytics and machine learning, and system informatics and control for manufacturing applications.