sequences from normal and anomalous flights.

The challenge here is to train the algorithms on the healthy condition monitoring data from the training dataset that are able to reliably detect anomalies in the validation data set (without any access to the anomalous time series during training and without any prior knowledge on the number and types of possible anomalies).

Pre-processing and Hyperrameter Settings for the Time-series to Image Encodings
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![**Encoding Examples**. \[fig:examples\]](Figures/examples_2.png){width="8cm"}

To evaluate the benefits of using the various time-series to image encodings, we compare the 6 selected encodings and their extensions between each other but also to a baseline that consists of raw 1D time series inputs without any encoding. For the 1D input, a 1D-CNN is applied instead of the 2D-CNN that is applied for all the time-series to image encodings. For consistency with the framework designed in Figure \[fig:framework\], we split the time series of length 61440 into 120 slices of size $l_1$ equal to 512. No additional operations such as smoothing or normalization is performed.

For all proposed encodings, we generate one image for each of the 120 sub-series of size 512. For computational efficiency, we set all parameters, when possible, to obtain images 