  Predicting faults before they occur helps to avoid potential safety hazards.
Furthermore, planning the required maintenance actions in advance reduces
operation costs. In this article, the focus is on electrochemical cells. In
order to predict a cell's fault, the typical approach is to estimate the
expected voltage that a healthy cell would present and compare it with the
cell's measured voltage in real-time. This approach is possible because, when a
fault is about to happen, the cell's measured voltage differs from the one
expected for the same operating conditions. However, estimating the expected
voltage is challenging, as the voltage of a healthy cell is also affected by
its degradation -- an unknown parameter. Expert-defined parametric models are
currently used for this estimation task. Instead, we propose the use of a
neural network model based on an encoder-decoder architecture. The network
receives the operating conditions as input. The encoder's task is to find a
faithful representation of the cell's degradation and to pass it to the
decoder, which in turn predicts the expected cell's voltage. As no labeled
degradation data is given to the network, we consider our approach to be a
self-supervised encoder. Results show that we were able to predict the voltage
of multiple cells while diminishing the prediction error that was obtained by
the parametric models by 53%. This improvement enabled our network to predict a
fault 31 hours before it happened, a 64% increase in reaction time compared to
the parametric model. Moreover, the output of the encoder can be plotted,
adding interpretability to the neural network model.
