A Novel Unsupervised Domain Adaptation Transformation Reconstructed Gated Recurrent Unit Framework Considering Prediction Uncertainty for Machinery Prognostics Under Variable Lubrication Conditions

Abstract

Abstract As critical components in industrial application scenarios, high-precision and high-confidence health assessment of rolling bearings attract more and more attention. Currently, predictive maintenance obtains outstanding achievements under the same object and working conditions. However, evaluation performances under variable working conditions and different specifications still need to be improved. This study zeroes in on the cross-domain prognostics of rotating machinery under oil and grease lubrication conditions. It proposes an unsupervised domain adaptation (DA) transform reconstruction GRU (UDATrGRU) prognostics framework, which captures the common degradation characteristics under different lubrication conditions through the designed second-order statistical quantity, facilitating the following high-precision predictions. To be specific, the vibration degradation features are first extracted through signal preprocessing and then input into UDATrGRU. The developed domain adaptation layer calculates high-dimensional projections between diverse data sets, and then corresponding degradation features are statistically aligned under the pressure of the designed quantity. Subsequently, time-series modeling and Bootstrap-based uncertainty estimations are carried out. Finally, lifecycle accelerated tests of the rolling bearing from PRONOSITA and ABLT-1A cross-validate the feasibility and effectiveness of the proposed machinery prognostics framework. The results are pretty promising: compared to existing methods, our UDATrGRU framework has achieved an improvement of at least 5.65% in R2 and a reduction of at least 21.5% in root mean squared error.