Predictive Maintenance (PdM) methods aim to facilitate the scheduling of maintenance work before equipment failure. In this context, detecting early faults in automated teller machines (ATMs) has become increasingly important since these machines are susceptible to various types of unpredictable failures. ATMs track execution status by generating massive event-log data that collect system messages unrelated to the failure event. Predicting machine failure based on event logs poses additional challenges, mainly in extracting features that might represent sequences of events indicating impending failures. Accordingly, feature learning approaches are currently being used in PdM, where informative features are learned automatically from minimally processed sensor data. However, a gap remains to be seen on how these approaches can be exploited for deriving relevant features from event-log-based data. To fill this gap, we present a predictive model based on a convolutional kernel (MiniROCKET and HYDRA) to extract features from the original event-log data and a linear classifier to classify the sample based on the learned features. The proposed methodology is applied to a significant real-world collected dataset. Experimental results demonstrated how one of the proposed convolutional kernels (i.e. HYDRA) exhibited the best classification performance (accuracy of 0.759 and AUC of 0.693). In addition, statistical analysis revealed that the HYDRA and MiniROCKET models significantly overcome one of the established state-of-the-art approaches in time series classification (InceptionTime), and three non-temporal ML methods from the literature. The predictive model was integrated into a container-based decision support system to support operators in the timely maintenance of ATMs.

翻译：预测性维护（PdM）方法旨在设备发生故障前安排维护工作。在此背景下，自动柜员机（ATM）易受各种不可预测故障影响，其早期故障检测日益重要。ATM通过生成海量事件日志数据来追踪执行状态，这些数据收集与故障事件无关的系统消息。基于事件日志预测机器故障面临额外挑战，主要在于提取可能代表预示故障事件序列的特征。因此，当前PdM中正采用特征学习方法，从最小处理的传感器数据中自动学习信息丰富的特征。然而，如何利用这些方法从基于事件日志的数据中提取相关特征仍存在研究空白。为填补该空白，我们提出一种基于卷积核（MiniROCKET和HYDRA）的预测模型，从原始事件日志数据中提取特征，并利用线性分类器基于学习到的特征对样本进行分类。所提方法应用于一个重要的真实世界收集数据集。实验结果表明，所提卷积核之一（即HYDRA）展现出最佳分类性能（准确率0.759，AUC值0.693）。此外，统计分析揭示，HYDRA和MiniROCKET模型显著超越了时间序列分类领域中一项已建立的最先进方法（InceptionTime）及文献中三种非时序机器学习方法。该预测模型已集成至基于容器的决策支持系统中，以支持运维人员对ATM进行及时维护。