A General-Purpose Anomalous Scenario Synthesizer for Rotary Equipment
Yip Fun Yeung, Ali Alshehri, Lois Wampler, Mikio Furokawa, Takayuki Hirano, Kamal Youcef‐Toumi · 2021
Data synthesizing is crucial for data-driven anomaly prognostics on physical machines. We propose the first general-purpose anomalous scenario synthesizer, GPASS, for rotary equipment. More specifically, we present a design of implementing modular rotational damping, large lateral force, with high-frequency range capability as fundamental modes of physical inputs. The GPASS is a general-purpose platform that can impose inputs independently or jointly, and generate an extensive range of anomalous scenarios on the same subject. Finally, it has the capability of capturing multi-variate sensor readings on the same anomalous event. Experimental results demonstrate that the synthesizer can dynamically and accurately introduce lateral force at specified magnitudes and frequencies, proving the effectiveness of the proposed device.