Jiahao Zhang
Anomaly detection on vehicle data: models and explainability.
Rel. Luca Cagliero, Francesco Vaccarino. Politecnico di Torino, Corso di laurea magistrale in Data Science And Engineering, 2023
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Abstract
Vehicle transportation is essential in today’s society. It facilitates the movement of people and goods at a reasonable cost and with high efficiency, particularly for short to medium distances. In this context, managing and preventing vehicle failures becomes crucial for key performance indicators (KPIs) such as safety, efficiency, and the reliability of modern transportation systems. As vehicles become increasingly sophisticated with advanced sensors and complex onboard systems, data collection tools play a crucial role in preventing and detecting failures to avoid subsequent system breakdowns that could impact the proper functioning of vehicles. In this thesis, we will introduce a new tool designed to provide companies with insightful analysis of specific failures.
This tool involves the development of a machine-learning model that can be applied directly to new vehicle data to identify potential anomalies, thereby preventing further failures
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