A novel approach for predictive maintenance combining GAF encoding strategies and deep networks

One of the most important challenge in the Industry 4.0 era is to provide advanced maintenance mechanisms able to avoid or minimize failures during production processes. In this paper, we propose a novel approach to the predictive maintenance problem that combines encoding strategies to convert temporal data into images and classical deep learning strategies. In particular, the basic idea is to exploit GAF (Gramian Angular Field) encoding to obtain images from time series related to production systems, which can be used in pre-trained convolutionary neural networks for better prediction performance and thus to create a more efficient and simple predictive maintenance scheme. Some experiments on several standard datasets show the advantages of the proposed techniques w.r.t. to other approaches in the literature.