A transferable neural network method for remaining useful life prediction

Abstract

Prognostics based on the deep learning model often assume that their training and testing data come from the same equipment with similar working conditions. However, a machine often has specific operating conditions for different tasks, which will cause significant divergence in its measurements. Generally, planned maintenance or model-based fault detections can be done first to collect very few suspension histories when the machine works in a new environment. Few suspension histories can help the prognostic model generalize to new environments. This paper proposes a transductive method to use limited suspension histories in transfer prognostics. Different from reported cross-domain prognostics that only align two-domain histories in a holistic manner, the proposed domain adaptation strategy simultaneously minimizes the distance between both marginal and conditional probability distributions in different domains. If the measurements have a clear degradation manifold, iterative learning will allow the model to get better and better pseudo predictions, thus guiding the prognostic model to learn generalized domain invariant features to deal with different working conditions. A heuristic method and a parallel framework are proposed to verify model parameters and uncertainties. The prognostic performance of the proposed approach is validated by using two case studies.