Rolling bearing fault diagnosis has garnered increased attention in recent years owing to its presence in rotating machinery across various industries, and an ever increasing demand for efficient operations. Prompt detection and accurate prediction of bearing failures can help reduce the likelihood of unexpected machine downtime and enhance maintenance schedules, averting lost productivity. Recent technological advances have enabled monitoring the health of these assets at scale using a variety of sensors, and predicting the failures using modern Machine Learning (ML) approaches including deep learning architectures. Vibration data has been collected using accelerated run-to-failure of overloaded bearings, or by introducing known failure in bearings, under a variety of operating conditions such as rotating speed, load on the bearing, type of bearing fault, and data acquisition frequency. However, in the development of bearing failure classification models using vibration data there is a lack of consensus in the metrics used to evaluate the models, data partitions used to evaluate models, and methods used to generate failure labels in run-to-failure experiments. An understanding of the impact of these choices is important to reliably develop models, and deploy them in practical settings. In this work, we demonstrate the significance of these choices on the performance of the models using publicly-available vibration datasets, and suggest model development considerations for real world scenarios. Our experimental findings demonstrate that assigning vibration data from a given bearing across training and evaluation splits leads to over-optimistic performance estimates, PCA-based approach is able to robustly generate labels for failure classification in run-to-failure experiments, and $F$ scores are more insightful to evaluate the models with unbalanced real-world failure data.

翻译：滚动轴承故障诊断因其存在于各行业旋转机械中，且对高效运行的需求日益增加，近年来受到越来越多的关注。及时检测和准确预测轴承故障有助于降低意外停机可能性，优化维修计划，避免生产力损失。近期技术进步使人们能够利用多种传感器大规模监测这些资产的健康状态，并使用包括深度学习架构在内的现代机器学习方法来预测故障。振动数据通过加速过载轴承的疲劳失效试验或引入已知故障的轴承，在转速、载荷、故障类型及数据采集频率等多种运行条件下采集。然而，在利用振动数据开发轴承故障分类模型时，用于评估模型的指标、用于模型评估的数据划分方法以及疲劳失效试验中故障标签生成方法缺乏共识。理解这些选择的影响对于可靠开发模型并在实际场景中部署至关重要。本研究利用公开振动数据集，论证了这些选择对模型性能的影响，并为真实世界场景提出模型开发建议。实验结果表明，将同一轴承的振动数据分配到训练集和测试集会导致过于乐观的性能估计；基于PCA的方法能够稳健地为疲劳失效试验中的故障分类生成标签；而F分数在评估存在不平衡真实故障数据的模型时更具洞察力。