is only defined with respect to a main class, and not in itself [@Zimek2017]. In the industry, it is not possible to define, and, therefore, to collect, representative samples of all possible types of anomalies. Consequently, ways need to be devised to train the anomaly detector on data which is almost exclusively collected in normal conditions [@michau2020feature]. Indeed, complex industrial systems are almost exclusively operating in healthy conditions, since they are otherwise maintained to avoid major consequences. Healthy operation data are usually highly available, and the task of anomaly detection can, therefore, be defined as the learning of a representation of the healthy operating conditions to detect any deviation from it. Previous works have shown that, for such approaches, monitoring the residuals of an auto-encoder trained on the main class could solve this issue [@Hu2017Fault; @Chouhan2019Network]. The recent developments in deep learning have however been driven by image processing, with some major success in the use of auto-encoders [@zhang2019ae2]. Also, recent works suggested several transformations of time series to images to benefit from the latest advances in supervised deep learning [@gondara2016medical; @krummenacher2017wheel; @sasirekha2020novel]. In addition, encoding time series to images can help to emphasize, capture or condense local patterns that 