A Sequence-to-Sequence Approach for Remaining Useful Lifetime Estimation Using Attention-augmented Bidirectional LSTM

Abstract We propose a novel sequence-to-sequence prediction approach for the estimation of the remaining useful lifetime (RUL) of technical components. The approach is based on deep recurrent neural network structures, namely bidirectional Long Short Term Memory (LSTM) networks, which we augment with an attention mechanism to allow for a more fine-grained information flow between the input and output sequence. Using the base architecture as a reference, we experiment with various forms of attention mechanisms as well as different forms of additional input embeddings. Further, we analyse the impact of the sequence length on the estimation quality. We apply our approach to the well known C-MAPSS data set previously serving as a benchmark dataset for RUL prediction. We obtain state of the art results on the data set and provide a thorough hyperparameter study that underlines, that more simple but well tuned architecture can achieve comparable or better performance than highly complex architectures.