  An essential task in predictive maintenance is the prediction of the
Remaining Useful Life (RUL) through the analysis of multivariate time series.
Using the sliding window method, Convolutional Neural Network (CNN) and
conventional Recurrent Neural Network (RNN) approaches have produced impressive
results on this matter, due to their ability to learn optimized features.
However, sequence information is only partially modeled by CNN approaches. Due
to the flatten mechanism in conventional RNNs, like Long Short Term Memories
(LSTM), the temporal information within the window is not fully preserved. To
exploit the multi-level temporal information, many approaches are proposed
which combine CNN and RNN models. In this work, we propose a new LSTM variant
called embedded convolutional LSTM (ECLSTM). In ECLSTM a group of different 1D
convolutions is embedded into the LSTM structure. Through this, the temporal
information is preserved between and within windows. Since the hyper-parameters
of models require careful tuning, we also propose an automated prediction
framework based on the Bayesian optimization with hyperband optimizer, which
allows for efficient optimization of the network architecture. Finally, we show
the superiority of our proposed ECLSTM approach over the state-of-the-art
approaches on several widely used benchmark data sets for RUL Estimation.
