A New Penalty Domain Selection Machine Enabled Transfer Learning for Gearbox Fault Recognition
Fei Shen, Hui Yun, Ruqiang Yan, Chuang Sun, Jiawen Xu
Abstract
The various structures and working conditions make gearbox fault recognition (GFR) more challenging. This article presents a new penalty domain selection machine (PDSM) enabled transfer learning for GFR study. The domain selection rules are designed using the band-selective independent component analysis to obtain the relation between different sensor locations and fault components for signal separation. Meanwhile, the initial penalty factors are calculated to speed up the PDSM process. For PDSM, the domain/signal penalty factors are added to the objective error function of original domain selection machine (DSM) to adapt varying working conditions and different sensor locations simultaneously. To solve the mixed optimization problem involved in PDSM, the Karush-Kuhn-Tucker conditions are utilized to transform it to a two-layer single problem. Experiments using drivetrain dynamics simulator prove that PDSM has higher diagnostic accuracy than other domain adaptation models. Meanwhile, it indicates faster convergence and stronger clustering capability than DSM.