Anomaly Detection for Heavy-duty Vehicles

Title	Anomaly Detection for Heavy-duty Vehicles
Summary	develop contextual and explainable anomaly detection algorithms for monitoring critical components and their efficiencies in heavy-duty vehicles; in collaboration with Volvo Group
Keywords
TimeFrame	Fall 2025 or Spring 2026
References	Li, Z., Zhu, Y., & Van Leeuwen, M. (2023). A survey on explainable anomaly detection. ACM Transactions on Knowledge Discovery from Data, 18(1), 1-54. Li, Z., & Van Leeuwen, M. (2023). Explainable contextual anomaly detection using quantile regression forests. Data Mining and Knowledge Discovery, 37(6), 2517-2563. Pasini, K., Khouadjia, M., Same, A., Trépanier, M., & Oukhellou, L. (2022). Contextual anomaly detection on time series: a case study of metro ridership analysis. Neural Computing and Applications, 34(2), 1483-1507. Han, X., Zhang, L., Wu, Y., & Yuan, S. (2023, October). On root cause localization and anomaly mitigation through causal inference. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management (pp. 699-708). Carvalho, J., Zhang, M., Geyer, R., Cotrini, C., & Buhmann, J. M. (2024). Invariant anomaly detection under distribution shifts: a causal perspective. Advances in Neural Information Processing Systems, 36. Fan, Y., Nowaczyk, S., & Antonelo, E. A. (2016, July). Predicting air compressor failures with echo state networks. In PHM Society European Conference (Vol. 3, No. 1). Gallicchio, C. (2024). Euler state networks: Non-dissipative reservoir computing. Neurocomputing, 579, 127411.
Prerequisites
Author
Supervisor	Yuantao Fan, TBD
Level	Master
Status	Open

Ensuring operational safety is crucial for commercial heavy-duty vehicles. The aim of this project is to design and develop contextual and explainable anomaly detection algorithms for monitoring critical components and their efficiencies in heavy-duty vehicles. Beyond safety considerations, cost-efficient and reliable maintenance strategies are essential for operating commercial vehicle fleets, where operational availability and total maintenance cost directly impact profitability.

By applying anomaly detection techniques to identify early signs of component degradation or failure, this project seeks to enable proactive and predictive maintenance. Such a system would minimize unplanned downtime, extend component lifespan, and reduce overall maintenance costs, thereby enhancing the safety, reliability, and economic performance of heavy-duty vehicle fleets. A promising research direction involves investigating representation learning techniques (such as contrastive learning, continual learning, federated learning etc.) to capture and encode key characteristics of time series data for anomaly detection. For instance, many state-of-the-art time methods utilize autoencoders, using learned embeddings in the latent features or reconstruction errors to compute anomaly scores. In addition, methods that are inherently explainable (e.g. causal graph/relations learned via causal inferences), that can be learned in an incremental setting (e.g. decision tree-based approaches) or computationally efficient (e.g. echo state network via reservoir computing), are of great interest as well. The developed approach will be evaluated on a real-world dataset collected from commercial vehicle fleets.

You will work with the Advanced Analytics Team (Volvo Group) and collaborate with domain experts, stakeholders in different tech streams.

Please contact Yuantao for more details.

Please apply via the following link (in Volvo portal):

https://jobs.volvogroup.com/job-invite/26403/