A Concept of the Real-time Diagnostic System for Prototype Engines - Architecture and Algorithm
Vitaly Promyslov, Stanislav Masolkin
2013
Abstract
The paper summarizes the main ideas and methods used in a software design of the real time diagnostic system for an advanced engines prototype test bed. The software architecture of the diagnostic systems is built on a top of the multiprocessor computer system which allows affectively performs various tasks. The SVM (support vector machine) algorithm is discussed from a point of view its real time implementation. The simulation results are presented and discussed.
References
- Vapnik, V., 1995. The Nature of Statistical Learning Theory, Springer-Verlag, New-York.
- Iverson, D., Martin, R., Schwabacher, M., Spirkovska, L, Mackey, R., Castle, P., et al., 2009-1909. General Purpose Data- Driven System Monitoring for Space Operations, AIAA Infotech@Aerospace Conference, AIAA, Washington, DC, 2009, AIAA paper.
- Schwabacher, M, Nikunj, Oza, Matthews, B., 2009. Unsupervised Anomaly Detection for Liquid-Fueled Rocket Propulsion Health Monitoring. Journal of aerospace computing, information, and communication. Vol. 6, July.
- Jeong, S, Kang, I., Jeong, M. K., Kong, D., 2012. A New Feature Selection Method for One-Class Classification Problems, IEEE Transactions on Systems, Man, Cybernetics, Part C, 42, pp. 1500- 1509.
- Kang, I. Jeong M. K., and Kong, D., 2012. A Differentiated One-Class Classification Method with Applications to Intrusion Detection, Expert Systems with Applications, 39, pp. 3899-3905.
- Tax, D. M. J., Duin, R. P. W., 1999. Support Vector Domain Description, Pattern Recognition Letters, Vol. 20, No. 1113, pp. 1191-1199.
- Chandola, V., Banerjee, A., Kumar, V., 2009. Anomaly Detection: A Survey, (to appear), ACM Computing Surveys.
- Markou, M., Singh, S., 2003. Novelty Detection: A Review. Part 1: Statistical Approaches, [Online], Signal Processing, Vol. 83, pp. 2481-2497. http://dx.doi.org/ 10.1016/j.sigpro.2003.07.018 (Accessed: 6 ?une 2013).
- Markou, M., Singh, S., 2003. Novelty Detection: A Review. Part 2, Neural Network Based Approaches, Signal Processing, Vol. 83, pp. 2499-2521.
- Chang, C., Lin, C., 2007. LIBSVM: a Library for Support Vector Machines, (Online), http://www.csie.ntu.edu.tw/ cjlin/papers/libsvm.pdf (Accessed: 6 ?une 2013).
- Runarsson, R. T., Unnthorsson, R., Johnson, T. M., 2003. Model Selection in One Class Nu-SVMS using RBF Kernels, 16-th Conference on Condition Monitoring and Diagnostic Engineering Management.
Paper Citation
in Harvard Style
Promyslov V. and Masolkin S. (2013). A Concept of the Real-time Diagnostic System for Prototype Engines - Architecture and Algorithm . In Proceedings of the 10th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-8565-70-9, pages 360-365. DOI: 10.5220/0004426703600365
in Bibtex Style
@conference{icinco13,
author={Vitaly Promyslov and Stanislav Masolkin},
title={A Concept of the Real-time Diagnostic System for Prototype Engines - Architecture and Algorithm},
booktitle={Proceedings of the 10th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2013},
pages={360-365},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004426703600365},
isbn={978-989-8565-70-9},
}
in EndNote Style
TY - CONF
JO - Proceedings of the 10th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - A Concept of the Real-time Diagnostic System for Prototype Engines - Architecture and Algorithm
SN - 978-989-8565-70-9
AU - Promyslov V.
AU - Masolkin S.
PY - 2013
SP - 360
EP - 365
DO - 10.5220/0004426703600365