Neural Turing Machines for the Remaining Useful Life estimation problem

Accurately estimating the amount of productive time remaining to a machine before it suffers a fault condition is a fundamental problem, which is commonly found in several contexts (e.g. mechanical systems) and can have great industrial, societal, and safety-related consequences. Recently, deep learning showed promising results and significant improvements towards a solution to the Remaining Useful Life estimation problem. In this paper, the usage of a sequence model called Neural Turing Machine (NTM), which can be seen as a computer'' that uses the available data to learn how to interact with an external memory, is thoroughly explored. In particular, even by using a single NTM as the key feature extraction component, more accurate solutions can be obtained when compared to widely used Long Short-Term Memory-based solutions. Moreover, such an improvement can be obtained while using fewer learnable parameters. The proposed approach is validated using sensor data of aircraft turbofan engines and particle filtration systems, obtaining competitive results to state-of-the-art techniques. Furthermore, the source code is released at https://github.com/aranciokov/NTMFor-RULEstimation to provide a strong baseline for the community, to support reproducibility and faster advancement in this field.

Automated summary

This paper explores the usage of Neural Turing Machine (NTM) in solving the problem of estimating the Remaining Useful Life. It is found that using a single NTM as the key feature extraction component can yield more accurate solutions compared to commonly used Long Short-Term Memory-based solutions. The proposed approach is validated using sensor data from aircraft turbofan engines and particle filtration systems, achieving competitive results against state-of-the-art techniques.