This paper introduces a classification method using in-context learning in time-series foundation models (TSFM). We show how data, which was not part of the TSFM training data corpus, can be classified without the need of finetuning the model. Examples are represented in the form of targets (class id) and covariates (data matrix) within the prompt of the model, which enables to classify an unknown covariate data pattern alongside the forecast axis through in-context learning. We apply this method to vibration data for assessing the health state of a bearing within a servo-press motor. The method transforms frequency domain reference signals into pseudo time-series patterns, generates aligned covariate and target signals, and uses the TSFM to predict probabilities how classified data corresponds to predefined labels. Leveraging the scalability of pre-trained models this method demonstrates efficacy across varied operational conditions. This marks significant progress beyond custom narrow AI solutions towards broader, AI-driven maintenance systems.

翻译：本文提出了一种利用时间序列基础模型（TSFM）上下文学习的分类方法。我们展示了如何对不属于TSFM训练数据集的样本进行分类，而无需对模型进行微调。在模型提示中，样本以目标（类别标识）和协变量（数据矩阵）的形式表示，这使得模型能够通过上下文学习，在预测轴上对未知的协变量数据模式进行分类。我们将此方法应用于振动数据，以评估伺服压力电机中轴承的健康状态。该方法将频域参考信号转换为伪时间序列模式，生成对齐的协变量与目标信号，并利用TSFM预测分类数据与预定义标签对应的概率。借助预训练模型的可扩展性，该方法在不同运行条件下均表现出有效性。这标志着从定制化狭义人工智能解决方案向更广泛的人工智能驱动维护系统迈出了重要进展。