  Rare events are occurrences that take place with a significantly lower
frequency than more common regular events. In manufacturing, predicting such
events is particularly important, as they lead to unplanned downtime,
shortening equipment lifespan, and high energy consumption. The occurrence of
events is considered frequently-rare if observed in more than 10% of all
instances, very-rare if it is 1-5%, moderately-rare if it is 5-10%, and
extremely-rare if less than 1%. The rarity of events is inversely correlated
with the maturity of a manufacturing industry. Typically, the rarity of events
affects the multivariate data generated within a manufacturing process to be
highly imbalanced, which leads to bias in predictive models. This paper
evaluates the role of data enrichment techniques combined with supervised
machine-learning techniques for rare event detection and prediction. To address
the data scarcity, we use time series data augmentation and sampling methods to
amplify the dataset with more multivariate features and data points while
preserving the underlying time series patterns in the combined alterations.
Imputation techniques are used in handling null values in datasets. Considering
15 learning models ranging from statistical learning to machine learning to
deep learning methods, the best-performing model for the selected datasets is
obtained and the efficacy of data enrichment is evaluated. Based on this
evaluation, our results find that the enrichment procedure enhances up to 48%
of F1 measure in rare failure event detection and prediction of supervised
prediction models. We also conduct empirical and ablation experiments on the
datasets to derive dataset-specific novel insights. Finally, we investigate the
interpretability aspect of models for rare event prediction, considering
multiple methods.
