Abstract--Correct identification of failure mechanisms is essential for manufacturers to ensure the quality of their products. Certain failures of printheads developed by Canon Production Printing can be identified from the behavior of individual nozzles, the states of which are constantly recorded and can form distinct patterns in terms of the number of failed nozzles over time, and in space in the nozzle grid. In our work, we investigate the problem of printhead failure classification based on a multifaceted dataset of nozzle logging and propose a Machine Learning classification approach for this problem. We follow the feature-based framework of time-series classification, where a set of time-based and spatial features was selected with the guidance of domain experts. Several traditional ML classifiers were evaluated, and the One-vs-Rest Random Forest was found to have the best performance. The proposed model outperformed an in-house rule-based baseline in terms of a weighted F1 score for several failure mechanisms. Identifying failure mechanisms is a critical part of industrial corrective maintenance for manufacturers to ensure the quality of their products [1].

Detecting intrusions and anomalies in Industrial Control Systems at early stages is important to prevent process failure. Operator errors, device or equipment failures, and other non-network events could lead to a critical state. As a result, these events can indirectly lead to anomalous network traffic, and, thus, a manually configured IDS that uses network traffic alone can generate false positives and false negatives. In this paper, we propose a novel approach that uses multimodal machine learning and incorporates both network data and device state information to improve the detection accuracy. Our methodology can detect anomalies as well as their root causes, which is essential.