Politecnico di Torino (logo)

Anomaly detection from vehicle data

Amri Myftija

Anomaly detection from vehicle data.

Rel. Luca Cagliero, Francesco Vaccarino. Politecnico di Torino, Corso di laurea magistrale in Data Science And Engineering, 2023

[img] PDF (Tesi_di_laurea) - Tesi
Restricted to: Repository staff only until 15 December 2026 (embargo date).
Licenza: Creative Commons Attribution Non-commercial No Derivatives.

Download (4MB)

Modern vehicles generate vast amounts of operational data that reflect their health and performance. When processed correctly, this data can guide manufacturers and technicians in performing targeted repairs and predictive maintenance. This not only enhances vehicle safety but also mitigates repair costs through the enabling of timely interventions. Currently, the predominant fault-detection systems are based on static rules formulated by domain experts. These rules, however, can only capture a limited scope of anomaly patterns. This thesis explores the application of machine-learning techniques for anomaly-detection within vehicle data. Beginning with an introduction to anomaly detection and time series analysis, the study then examines various unsupervised anomaly detection algorithms, from both theoretical and practical perspectives. It dedicates a special focus to DAMP, a novel anomaly detection algorithm based on the Matrix-Profile data structure. Finally, the thesis presents a real-life use case developed in partnership with FPT Industrial. The proposed model, a complete data-processing pipeline based on the DAMP algorithm yields impressive results on the case study.

Relators: Luca Cagliero, Francesco Vaccarino
Academic year: 2023/24
Publication type: Electronic
Number of Pages: 66
Corso di laurea: Corso di laurea magistrale in Data Science And Engineering
Classe di laurea: New organization > Master science > LM-32 - COMPUTER SYSTEMS ENGINEERING
Aziende collaboratrici: FPT Industrial Spa
URI: http://webthesis.biblio.polito.it/id/eprint/29363
Modify record (reserved for operators) Modify record (reserved for operators)