Estimating in a reliable way the Remaining Useful Life (RUL) of a mechanical component is a fundamental task in the field of Prognostics and Health Management (PHM). In recent years a greater availability of high quality sensors and easiness of data gathering gave rise to data-driven models based on deep learning for this task, which has recently seen the introduction of "dual-stream" architectures. In this paper we propose a dual-stream architecture to address the RUL estimation problem through the exploitation of a Neural Turing Machine (NTM) and a Multi-Head Attention (MHA) mechanism. The NTM is a content-based memory addressing system which gives each of the streams the ability to access to and interact with the memory and acts as a fusion technique. The MHA is an attention mechanism added as a mean for our architecture to identify the existing relations between different sensor data in order to reveal hidden patterns among them. To evaluate the performance of our model, we considered the C-MAPSS dataset, a benchmark dataset published by NASA consisting of several time series related to the life of turbofan engines. We show that our approach achieves the best prediction score (which measures the safety of the predictions) in the available literature on two of the C-MAPSS subdatasets.
Remaining useful life, neural networks, neural turing machine, attention, turbofan
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