An Asynchronous Gated Recurrent Network for Estimating Critical Transition of Bearing Deterioration

The service performance of an industrial bearing may deteriorate sharply after a sudden transition which is very difficult to be captured prior to its appearance. It is accordingly valuable to estimate this critical transition for avoiding serious catastrophes caused by the faulty bearing. To address this issue, an asynchronous gated recurrent network (AGRN) is proposed to estimate the critical transition of the bearing deterioration. A gated recurrent unit (GRU) is first developed to extract prognostic data as features using few early samples. Chi-square distributions of the squared prediction error of the residuals and the variation of principal components calculated from the features are fused as a statistic series, where the critical threshold is calculated adaptively. Another GRU is proposed to forecast deterioration process using fused statistic series. The forthcoming transition can be estimated by the process with the critical threshold at the initial operation. The present AGRN is evaluated by lifecycle experiments of three bearings, all from public benchmark datasets. Results show that the proposed method is robust in offering maintenance response time for bearings before transiting to critical failures.