A Two-stage Deep Learning Based Balancing Method for Rotating Machinery


 Purpose - Balancing is essential to all rotating machinery. To make the balancing process convenient and costless, new balancing technologies are needed. In this work, a two-stage deep learning based balancing method is proposed and validated. Design/methodology/approach - The architecture of the method is described. The whole balancing method has two stages. The first stage identifies the unbalance force vector and the second stage further identi…es the correlation masses and phases from the unbalanced force series. The deep learning based modules can be trained by one-run response data and labeled support force data only. Findings - Both numerical and experimental balancing performances are reasonable and comparative. The performances indicate that the proposed method is validate and robust. Originality - The proposed method combines deep learning technology with rotor dynamics knowledge. The proposed method achieves good performance without weight trail process and provides a competitive approach on rotor balancing technology.