A Physics-Specific Change Point Detection Method Using Torque Signals in Pipe Tightening Processes

Abstract

Change point detection in torque signals is critical for quality inspection in pipe tightening processes. This study proposes a novel approach by integrating dynamic time warping and tightening physics to reduce false detections and accurately identify true change points, enhancing process monitoring and product quality assurance.

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.