Document Type
Workshop
Abstract
In the age of Industry 4.0 and smart automation, unplanned downtime is costing industries over $50 billion annually. Even with preventive maintenance, industries like automotive lose more than $2 million per hour due to downtime caused by unexpected or "rare'' events. The extreme rarity of these events makes their detection and prediction a significant challenge for AI practitioners. Factors such as the lack of high-quality data, methodological gaps in the literature, and limited practical experience with multimodal data exacerbate the difficulty of rare event detection and prediction. This lab will provide hands-on experience to learn how to address these challenges by exploring the entire lifecycle of rare event analysis, from data generation and preprocessing to model development and evaluation. The development of a \textit{process ontology} and its use for user-level explanations will also be demonstrated. Participants will be introduced to the limited publicly available datasets and, more importantly, gain hands-on experience with a newly developed multi-modal dataset designed explicitly for rare event prediction. Through several hands-on sessions, participants will learn how to generate such a high-quality dataset and the practical use of this dataset to develop rare event prediction models. Those interested in developing AI models involving diverse multimodal data for other applications will also benefit from participation.
Publication Info
The 38th Annual AAAI Conference on Artificial Intelligence, ed. Association for the Advancement of Artificial Intelligence (AAAI), 2025.
Rights
2025, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
APA Citation
Shyalika, C., Wickramarachchi, R., Venkataramanan, R., Patel, D., & Sheth, A. (2025). Developing explainable multimodal AI models with hands-on lab on the life-cycle of rare event prediction in manufacturing. Proceedings of the AAAI Conference on Artificial Intelligence (AAAI-25).