Time series anomaly detection for Heavy-duty vehicles (in collaboration with Volvo)
| Title | Time series anomaly detection for Heavy-duty vehicles (in collaboration with Volvo) |
|---|---|
| Summary | Detecting anomalies in multivariate time series data via learned representations |
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| References | - 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.
- Wang, Z., & Veitch, V. (2022). A unified causal view of domain invariant representation learning. -Ikonomovska, E., Gama, J., & Džeroski, S. (2015). Online tree-based ensembles and option trees for regression on evolving data streams. Neurocomputing, 150, 458-470. - Muallem, A., Shetty, S., Pan, J. W., Zhao, J., & Biswal, B. (2017). Hoeffding tree algorithms for anomaly detection in streaming datasets: A survey. Journal of Information Security, 8(4). - 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. - Foumani, N. M., Tan, C. W., Webb, G. I., Rezatofighi, H., & Salehi, M. (2024). Series2vec: similarity-based self-supervised representation learning for time series classification. Data Mining and Knowledge Discovery, 1-25. - Baevski, A., Hsu, W. N., Xu, Q., Babu, A., Gu, J., & Auli, M. (2022, June). Data2vec: A general framework for self-supervised learning in speech, vision and language. In International Conference on Machine Learning (pp. 1298-1312). PMLR. |
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| Supervisor | Yuantao & TBD |
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| Status | Open |
In this project, we will develop and 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. Methods that are inherently explainable (e.g. causal relations learned via causal inferences), can be learned in an incremental setting (e.g. online learning with 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 and compared with a few time series embedding methods on a real-world dataset collected from EVs.
This project is a collaboration with Volo, you will work closely with the Advanced Analytics Team at Volvo Group Technology.
Please contact Yuantao for more details.
