Time series anomaly detection for heavy-duty vehicles
| Title | Time series anomaly detection for heavy-duty vehicles |
|---|---|
| Summary | Detecting anomalies in multivariate time series data collected from vehicle operations |
| Keywords | |
| TimeFrame | Fall 2023 |
| References | Ruiz, C., Menasalvas, E., & Spiliopoulou, M. (2009). C-denstream: Using domain knowledge on a data stream. In Discovery Science: 12th International Conference, DS 2009, Porto, Portugal, October 3-5, 2009 12 (pp. 287-301). Springer Berlin Heidelberg.
Cao, F., Estert, M., Qian, W., & Zhou, A. (2006, April). Density-based clustering over an evolving data stream with noise. In Proceedings of the 2006 SIAM international conference on data mining (pp. 328-339). Society for industrial and applied mathematics. Fan, Y., Nowaczyk, S., & Antonelo, E. A. (2016). Predicting air compressor failures with echo state networks. In PHM Society European Conference (Vol. 3, No. 1). Fan, Y., Nowaczyk, S., & Rögnvaldsson, T. (2020). Transfer learning for remaining useful life prediction based on consensus self-organizing models. Reliability Engineering & System Safety, 203, 107098. Hendrickx, K., Meert, W., Mollet, Y., Gyselinck, J., Cornelis, B., Gryllias, K., & Davis, J. (2020). A general anomaly detection framework for fleet-based condition monitoring of machines. Mechanical Systems and Signal Processing, 139, 106585. |
| Prerequisites | |
| Author | |
| Supervisor | Yuantao Fan |
| Level | |
| Status | Internal Draft |
This project will explore and develop algorithms to detect anomalies in multi-variate time series data, collected from heavy-duty vehicles. The idea is to identify different operating modes using stream clustering algorithms and discover anomalies in each cluster, i.e. combing DenStream and COSMO. The goal is to discover anomalous events in advance to enhance vehicle operation safety.
