  Accurate estimation of remaining useful life (RUL) of industrial equipment
can enable advanced maintenance schedules, increase equipment availability and
reduce operational costs. However, existing deep learning methods for RUL
prediction are not completely successful due to the following two reasons.
First, relying on a single objective function to estimate the RUL will limit
the learned representations and thus affect the prediction accuracy. Second,
while longer sequences are more informative for modelling the sensor dynamics
of equipment, existing methods are less effective to deal with very long
sequences, as they mainly focus on the latest information. To address these two
problems, we develop a novel attention-based sequence to sequence with
auxiliary task (ATS2S) model. In particular, our model jointly optimizes both
reconstruction loss to empower our model with predictive capabilities (by
predicting next input sequence given current input sequence) and RUL prediction
loss to minimize the difference between the predicted RUL and actual RUL.
Furthermore, to better handle longer sequence, we employ the attention
mechanism to focus on all the important input information during training
process. Finally, we propose a new dual-latent feature representation to
integrate the encoder features and decoder hidden states, to capture rich
semantic information in data. We conduct extensive experiments on four real
datasets to evaluate the efficacy of the proposed method. Experimental results
show that our proposed method can achieve superior performance over 13
state-of-the-art methods consistently.
