A faulty simulation model guided Ramanujan Digital twin architecture for rotating machine health monitoring.

Abstract

The conventional widely-used health monitoring methods for rotating machines have shortcomings such as the reliance on the selection of the preset parameters. Also, the strong noise interference caused by factors such as transmission path hinders the practical application of many fault feature extraction methods. To overcome these gaps, the digital twin notion is introduced and a new digital twin architecture called the Ramanujan Digital Twin (RDT) is designed. The Ramanujan Periodic Transform (RPT) model is employed to isolate the potential fault feature. For each frame in the whole life cycle of the rotating machine, the high-fidelity simulation model is constructed. Once the high-fidelity simulation-induced virtual sample is obtained, the RPT will be used to provide guidance information about the potential fault. With this information, the potential fault feature can be extracted without preset parameter selection. A health indicator (HI) can be constructed to perform multiple service end tasks including health monitoring and early fault prediction. Two case studies are carried out and the results show the proposed method can not only extract the potential fault feature more effectively with less noise interference but also monitor and predict the potential early fault earlier than fault log.