of this paper is organised as follows: Section \[sec:anode\] describes the proposed anomaly detection framework; Section \[sec:encod\] presents the different image encodings used to represent time series data; This methodology is applied with the different image encodings to an industrial use case introduced in Section \[sec:casestudy\]; finally, we discuss the methods and the perspectives.

![image](Figures/Framework/framework_and_network.png){width="\textwidth"}

Framework for Anomaly Detection {#sec:anode}
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The proposed framework, illustrated in Figure \[fig:framework\], consists in the detection of anomalous input reconstruction based on an auto-encoder neural network with different types on time-series to image encodings as input. The underlying idea is to train an autoencoder to reconstruct its own input while compressing it to a lower dimensional latent space, acting as a bottleneck. The network will learn to reconstruct as well as possible its input data (either time series or images) following the same distribution as the ones it has been trained on. If the the auto-encoder is trained on healthy data, its reconstruction ability will be poor if presented with anomalous conditions that deviate from the healthy conditions used during training. Monitoring the reconstruction error with respect to a threshold allows the detection of such anomalous patterns.

Since the original time series in the 