Robotic Method and Instrument to Efficiently Synthesize Faulty Conditions and Mass-Produce Faulty-Conditioned Data for Rotary Machines

Abstract

Condition synthesis is vital for generating data for fault detection and diagnosis studies. Traditional methods rely heavily on human labor. This study proposes a robotic method and its instru-ment to efficiently synthesize faulty conditions and mass-produce data to develop fault detection and diagnosis algorithms. The first contribution is the formalization of a new approach called Robotic Condition Synthesis, which shifts the traditionally labor-intensive task of condition synthesis to a robot-based force control task. The second contribution is developing a new robotic manipulator, which is more effective than current lab-grade robots for the tasks involved in the Robotic Condition Synthesis. The third contribution is empirical evidence of the superiority of this new robot in performing the Robotic Condition Synthesis tasks. This study also explores the potential of the new robot by conducting a three-dimensional system identification of a rotordynamic plant, which lays the foundation for more advanced Robotic Condition Synthesis policies in the future.