An HMM-based change detection method for intelligent embedded sensors

Proceedings of the International Joint Conference on Neural Networks

Volumn , Issue , 2012, Pages

  Alippi, Cesare ^a   Ntalampiras, Stavros ^a   Roveri, Manuel ^a  

^a POLITECNICO DI MILANO   (Italy)

Author keywords

change detection test; dynamic process; hidden Markov model; intelligent sensor networks

Indexed keywords

CHANGE DETECTION; CONCEPT DRIFTS; DATA STREAM; DYNAMIC PROCESS; EMBEDDED SENSORS; FALSE NEGATIVE RATE; FALSE POSITIVE; GENERIC DATA; INTELLIGENT SENSOR NETWORKS; LINEAR DYNAMICS; LOG LIKELIHOOD; MULTI SENSOR; PATTERN STRUCTURE; PROBABILISTIC PATTERNS; SENSOR MEASUREMENTS; THRESHOLDING; TIME-INVARIANT MODELS;

DATA COMMUNICATION SYSTEMS; HIDDEN MARKOV MODELS; NEURAL NETWORKS;

SIGNAL DETECTION;

EID: 84865092865     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IJCNN.2012.6252610     Document Type: Conference Paper

References (15)

1. R. Isermann, Fault-diagnosis systems: an introduction from fault detection to fault tolerance. Springer Verlag, 2006.
2. V. Venkatasubramanian, R. Rengaswamy, K. Yin, and S. N. Kavuri, "A review of process fault detection and diagnosis: Part i: Quantitative model-based methods," Computers Chemical Engineering, vol. 27, no. 3, pp. 293 - 311, 2003. [Online]. Available: http://www.sciencedirect.com/science/ article/pii/S0098135402001606
3. W. Shewhart, Economic control of quality of manufactured product. American Society for Qualit, 1931, vol. 509.
4. T. Lai, "Sequential analysis: some classical problems and new challenges," Statistica Sinica, vol. 11, no. 2, pp. 303-350, 2001.
5. M. Basseville, I. Nikiforov et al., Detection of abrupt changes: theory and application. Prentice Hall Englewood Cliffs, 1993, vol. 15.
6. K. Goebel and W. Yan, "Correcting sensor drift and intermittency faults with data fusion and automated learning," Systems Journal, IEEE, vol. 2, no. 2, pp. 189-197, 2008.
7. J. Frolik, M. Abdelrahman, and P. Kandasamy, "A confidence-based approach to the self-validation, fusion and reconstruction of quasiredundant sensor data," Instrumentation and Measurement, IEEE Transactions on, vol. 50, no. 6, pp. 1761-1769, 2001.
8. T. Böhme, C. Cox, N. Valentin, and T. Denoeux, "Comparison of autoassociative neural networks and kohonen maps for signal failure detection and reconstruction," Intelligent Engineering Systems through Artificial Neural Networks, vol. 9, pp. 637-644, 1991.
9. M. Napolitano, D. Windon, J. Casanova, M. Innocenti, and G. Silvestri, "Kalman filters and neural-network schemes for sensor validation in flight control systems," Control Systems Technology, IEEE Transactions on, vol. 6, no. 5, pp. 596-611, 2002.
10. T. Guo and J. Nurre, "Sensor failure detection and recovery by neural networks," in Neural Networks, 1991., IJCNN-91-Seattle International Joint Conference on, vol. 1. IEEE, 1991, pp. 221-226.
11. R. Dunia, S. Qin, T. Edgar, and T. McAvoy, "Identification of faulty sensors using principal component analysis," AIChE Journal, vol. 42, no. 10, pp. 2797-2812, 1996.
12. S. Wellington, J. Atkinson, and R. Sion, "Sensor validation and fusion using the nadaraya-watson statistical estimator," in Information Fusion, 2002. Proceedings of the Fifth International Conference on, vol. 1. IEEE, 2002, pp. 321-326.
13. L. R. Rabiner, "A tutorial on hidden markov models and selected applications in speech recognition," Proceedings of the IEEE, pp. 257-286, 1989.
14. Torch machine learning library. [Online]. Available: http://www.torch.ch/
15. C. Alippi, G. Boracchi, A. Marullo, and M. Roveri, "A step towards the prediction of a rock collapse: Analysis of micro-acoustic bursts," in IEEE Sensors Conference, 2011.