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.

评估数据丰富技术与监督机器学习技术在罕见事件检测和预测中的作用，并发现数据丰富程序可以显著提高罕见故障事件的检测和预测性能。

评估数据丰富方法在制造业罕见事件分析中的作用

Evaluating the Role of Data Enrichment Approaches Towards Rare Event  Analysis in Manufacturing

Rare event prediction involves identifying and forecasting events with a low
probability using machine learning and data analysis. Due to the imbalanced
data distributions, where the frequency of common events vastly outweighs that
of rare events, it requires using specialized methods within each step of the
machine learning pipeline, i.e., from data processing to algorithms to
evaluation protocols. Predicting the occurrences of rare events is important
for real-world applications, such as Industry 4.0, and is an active research
area in statistical and machine learning. This paper comprehensively reviews
the current approaches for rare event prediction along four dimensions: rare
event data, data processing, algorithmic approaches, and evaluation approaches.
Specifically, we consider 73 datasets from different modalities (i.e.,
numerical, image, text, and audio), four major categories of data processing,
five major algorithmic groupings, and two broader evaluation approaches. This
paper aims to identify gaps in the current literature and highlight the
challenges of predicting rare events. It also suggests potential research
directions, which can help guide practitioners and researchers.

稀有事件预测是使用机器学习和数据分析识别和预测低概率事件。由于数据分布不均衡，普通事件的频率远远超过稀有事件，需要在机器学习流程中的每个步骤中使用专门的方法，从数据处理到算法到评估协议。该论文全面综述了稀有事件预测的当前方法在四个方面：稀有事件数据，数据处理，算法方法和评估方法。它旨在找出当前文献中的差距并突出预测稀有事件的挑战。它还提出了潜在的研究方向，可帮助指导从业人员和研究人员。