Recurrent neural networks (RNNs) such as LSTM and GRU are not new to the field of prognostics. However, the performance of neural networks strongly depends on their architectural structure. In this work, we investigate a hybrid network architecture that is a combination of recurrent and feed-forward (conditional) layers. Two networks, one recurrent and another feed-forward, are chained together, with inference and weight gradients being learned using the standard back-propagation learning procedure. To better tune the network, instead of using raw sensor data, we do some preprocessing on the data, using mostly simple but effective statistics (researched in previous work). This helps the feature extraction phase and eases the problem of finding a suitable network configuration among the immense set of possible ones. This is not the first proposal of a hybrid network in prognostics but our work is novel in the sense that it performs a more comprehensive comparison of this type of architecture for different RNN layers and number of layers. Also, we compare our work with other classical machine learning methods. Evaluation is performed on two real-world case studies from the aero-engine industry: one involving a critical valve subsystem of the jet engine and another the whole reliability of the jet engine. Our goal here is to compare two cases contrasting micro (valve) and macro (whole engine) prognostics. Our results indicate that the performance of the LSTM and GRU deep networks are significantly better than that of other models.
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Recurrent Neural Networks, Prognostics, Aeronautics, Real-World Case Studies
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