Multiscale deep bidirectional gated recurrent neural networks based prognostic method for complex non-linear degradation systems

Abstract Reliable estimation of the remaining useful life (RUL) of complex engineered systems plays a vital role in avoiding undue maintenance situations while guaranteeing system safety. However, efficient tracking of RUL often gets hampered by the prerequisite for prior knowledge regarding degradation characteristics of critical components, which are not available in most cases. Additionally, machine learning techniques face difficulties in adapting and modeling degradation trends in the presence of equipment’s complex working environments. To address these issues, we present a novel data-driven feature learning approach based on a multi-scale deep bidirectional gated recurrent neural network (MDBGRU). The MDBGRU network can: i) automatically learns both local and global information in addition to temporal variations in the multivariate sensor data; ii) capture salient discriminative features characterizing the system complexity; iii) overcome the pre-expertise requirement on multiple sub-components of the system; and iv) curb shortcomings of machine learning methods. Extensive experiments are performed on the C-MAPSS dataset to evaluate the prognostic capability of the proposed method. Compared with the existing works, our network attains enhanced prediction accuracy with an overall improvement of 13.54%, suggesting this as a new and promising RUL estimation approach.