dataset are comparably long (several thousands of values), it raises two types of challenges. First, since the monitored residual is an aggregated measure over the reconstruction, localised anomalies may be unnoticeable at the global scale. Second, for most encodings, computational time and size evolve according to a power law of the input size. To overcome these two challenges, we propose to first sub-divide the time series into smaller sub-series of size $l_1$. These sub-series are then encoded to images of size $l_2\times l_2$ which are reconstructed by the neural network and their residuals computed and aggregated. We define the residual of the $k$-th slice, $\mathrm{Res}_k$, as the $\ell_1$-norm of the difference between the input, $x_{\mathrm{or}}$, of size $l$ and its reconstruction, $x_{\mathrm{rc}}$ as: $$\mathrm{Res}_k = \sum_{i=1}^{l}(|x_{\mathrm{or}}^i - x_{\mathrm{rc}}^i|),
    \label{eq:residual}$$ where $l=l_1$ for the baseline model using the raw sub-series without any encodings as input to a one dimensional CNN, and $l=l_2\times l_2$ for all the other cases.

During training, only time series from healthy conditions are used. Based on the distribution of the residuals of all sub-series, we extract the 99-th percentile to define the detection threshold $\tau$. In the test set, for each time series, we monitor the maximum 