  Non-neural Machine Learning (ML) and Deep Learning (DL) models are often used
to predict system failures in the context of industrial maintenance. However,
only a few researches jointly assess the effect of varying the amount of past
data used to make a prediction and the extension in the future of the forecast.
This study evaluates the impact of the size of the reading window and of the
prediction window on the performances of models trained to forecast failures in
three data sets concerning the operation of (1) an industrial wrapping machine
working in discrete sessions, (2) an industrial blood refrigerator working
continuously, and (3) a nitrogen generator working continuously. The problem is
formulated as a binary classification task that assigns the positive label to
the prediction window based on the probability of a failure to occur in such an
interval. Six algorithms (logistic regression, random forest, support vector
machine, LSTM, ConvLSTM, and Transformers) are compared using multivariate
telemetry time series. The results indicate that, in the considered scenarios,
the dimension of the prediction windows plays a crucial role and highlight the
effectiveness of DL approaches at classifying data with diverse time-dependent
patterns preceding a failure and the effectiveness of ML approaches at
classifying similar and repetitive patterns preceding a failure.
