Anomaly Detection for Heavy-duty Vehicles

Title	Anomaly Detection for Heavy-duty Vehicles
Summary	develop context-aware and explainable anomaly detection algorithms for monitoring critical components and their efficiencies in heavy-duty vehicles
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 important for commercial heavy-duty vehicles. The aim of this project is to design and develop context-aware 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 critical for the effective operation of commercial vehicle fleets, where operational availability and total maintenance cost directly influence overall 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 is to explore the use of representation learning methods, e.g. deep learning-based approaches (including time series embedding methods), that can capture and encode key characteristics of time series data for anomaly detection. For instance, autoencoders can be trained, and learned embeddings in the latent features or reconstruction errors can be used to compute the anomaly score. Methods that are inherently explainable (e.g. causal relations learned via causal inferences), 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 interests as well. The developed approach will be evaluated and compared with sota methods on a real-world dataset collected from commercial heavy-duty vehicles.

The plan is to work with the Advanced Analytics Team (Volvo Group Technology) and have the opportunity to collaborate with domain experts and stakeholders.

Please contact Yuantao for more details.