Condition-based maintenance (CBM) has emerged as a proactive strategy for determining the best time for maintenance activities. In this paper, a case of a milling process with imperfect maintenance at a German automotive manufacturer is considered. Its major challenge is that only data with missing labels are available, which does not provide a sufficient basis for classical prognostic maintenance models. To overcome this shortcoming, a data science study is carried out that combines several analytical methods, especially from the field of machine learning (ML). These include time-domain and time–frequency domain techniques for feature extraction, agglomerative hierarchical clustering and time series clustering for unsupervised pattern detection, as well as a recurrent neural network for prognostic model training. With the approach developed, it is possible to replace decisions that were made based on subjective criteria with data-driven decisions to increase the tool life of the milling machines. The solution can be employed beyond the presented case to similar maintenance scenarios as the basis for decision support and prognostic model development. Moreover, it helps to further close the gap between ML research and the practical implementation of CBM.

中文翻译：

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带有缺失标签的预测模型开发

基于状态的维护 (CBM) 已成为确定维护活动最佳时间的主动策略。在本文中，考虑了德国汽车制造商维护不完善的铣削过程的案例。它的主要挑战是只有缺少标签的数据可用，这不能为经典的预后维护模型提供足够的基础。为了克服这个缺点，进行了一项数据科学研究，该研究结合了多种分析方法，尤其是机器学习 (ML) 领域的分析方法。其中包括用于特征提取的时域和时频域技术、用于无监督模式检测的凝聚层次聚类和时间序列聚类，以及用于预测模型训练的循环神经网络。随着方法的发展，可以将基于主观标准的决策替换为数据驱动的决策，以延长铣床的刀具寿命。该解决方案可以在所呈现的案例之外应用于类似的维护场景，作为决策支持和预测模型开发的基础。此外，它有助于进一步缩小 ML 研究与 CBM 实际实施之间的差距。