A hybrid-logic approach towards fault detection in complex cyber-physical systems



Published Oct 10, 2010
Nisheeth Srivastava Jaideep Srivastava


Existing data mining approaches to complex systems anomaly detection use uni-variate and/or multi-variate statistical hypothesis testing to assign anomaly scores to data streams associated with system components. The former approach assumes statistical independence of individual components, while the latter assumes substantial global systemic correlation. As a compromise between these two epistemological extremes, we present a data-mining approach hybridizing existing statistical techniques with theorem-proving methods to create a novel algorithm for anomaly detection, diagnosis and control in complex systems. Our algorithm takes sensor inputs from physical sensors providing system subcomponent performance data and outputs (i) a global systemic risk indicator and (ii) possible diagnosis hypotheses. We present results on three different systems, and in comparison with current state-of-the-art fault detection algorithms to demonstrate the viability of our approach. We find that our algorithm proves robust towards increased data dimensionality in contrast with existing clustering-based fault detection methods and can also detect contextual faults that are undetectable using existing statistical techniques.

How to Cite

Srivastava, N., & Srivastava, J. (2010). A hybrid-logic approach towards fault detection in complex cyber-physical systems. Annual Conference of the PHM Society, 2(1). https://doi.org/10.36001/phmconf.2010.v2i1.1888
Abstract 97 | PDF Downloads 75



complex systems, multivariate statistical analysis, real-time fault detection

Baragona, R., & Battaglia, F. (2007). Outlier detection in multivariate time series by Independent Component Analysis. Neural Computation, 19, 19621984.

Chandola, V., Banerjee, A., & V., K. (2009). Anomaly detection: A survey. ACM Computing survey, 41.

Cheng, H., Tan, P., Potter, C., & Klooster, S. (2009). Detection and Characterization of Anomalies in Multivariate Time Series. In Proceedings of the SIAM Data Mining Conference.

Houle, M., Kriegel, H., Kroger, P., Schubert, E., & Zimek, A. (2010). Can Shared-Neighbor Distances Defeat the Curse of Dimensionality? In Proceedings of the 21th International Conference on Scientific and Statistical Database Management.

Iverson, D. (2004). Inductive system health monitoring. In International Conference on Artificial Intelligence (IC-AI04).

Mengshoel, O., Darwiche, A., Cascio, K., Chavira, M., Poll, S., & Uckun, S. (2008). Diagnosing Faults in Electrical Power Systems of Spacecraft and Aircraft. In Proc. of the Twentieth Innovative Applications of Artificial Intelligence, Conference (IAAI08).

NASA-Dashlink. (n.d.). Retrieved on 01/12/2010 from.https://c3.ndc.nasa.gov/dl/data/ adapt-an-electrical-power-system -testbed/.

Neal, R., & Hinton, G. E. (1998). A View Of The Em Algorithm That Justifies Incremental, Sparse, And Other Variants. In Learning in Graphical Models (p. 355-368). Kluwer Academic Publishers.

Poll, S., Patterson-Hine, A., Camisa, J., Garcia, D., Hall, D., Lee, C., et al. (2007). Advanced diagnostics and prognostics testbed. In Proceedings of the 18th International Workshop on Principles of Diagnosis (DX-07) (p. 178-185).

Prover9/Mace4. (n.d.). Retrieved on 06/07/2010 from.http://www.cs.unm.edu/ ˜mccune/ prover9/.
Technical Research Papers