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

Automatic Remaining Useful Life Estimation Framework with Embedded Convolutional LSTM as the Backbone