Enfoque para Aislamiento de Fallas en Tiempo Continuo a Partir de Datos Muestreados

Resumen: En el presente trabajo, se considera un enfoque en tiempo continuo para aislamiento de fallas basado en observador a partir de datos entrada y salida. El objetivo del algoritmo propuesto es mostrar las ventajas con respecto a resultados anteriores referentes a aislamiento de fallas, así como la capacidad de considerar una clase de sistemas no lineales. Se muestra un ejemplo utilizando el algoritmo para probar su eficiencia, además de la justificación de los resultados.

¿Cómo citar?

Luis Angel Sánchez Rivera, Efraín Alcorta García & Ivon Elena Leal Leal. Enfoque para Aislamiento de Fallas en Tiempo Continuo a partir de Datos Muestreados. Memorias del Congreso Nacional de Control Automático, pp. 19-24, 2018.

Palabras clave

Diagnóstico de fallas, Procesamiento de datos, Observadores, Problemas de desacoplo, Sistemas no lineales

• Blanke, M., Kinnaert, M., Lunze, J., Staroswiecki, M., and Schr¨oder, J. (2006). Diagnosis and fault-tolerant control, volume 2. Springer.
• Calado, J., Korbicz, J., Patan, K., Patton, R.J., and Da Costa, J.S. (2001). Soft computing approaches to fault diagnosis for dynamic systems. European Journal of Control, 7(2-3), 248–286.
• Chen, J. and Patton, R.J. (2012). Robust model-based fault diagnosis for dynamic systems, volume 3. Springer Science & Business Media.
• Ding, S.X. (2013). Model-based fault diagnosis techniques 2nd edition. Springer, 2nd edition.
• Ding, S.X. (2014a). Data-driven design of fault diagnosis and fault-tolerant control systems. Springer Science & Business Media.
• Ding, S. (2014b). Data-driven design of monitoring and diagnosis systems for dynamic processes: A review of subspace technique based schemes and some recent results. Journal of Process Control, 24(2), 431–449.
• Ding, S., Zhang, P., Naik, A., Ding, E., and Huang, B. (2009). Subspace method aided data-driven design of fault detection and isolation systems. Journal of process control, 19(9), 1496–1510.
• Dong, J. and Verhaegen, M. (2012). Identification of fault estimation filter from i/o data for systems with stable inversion. IEEE Transactions on Automatic Control, 57(6), 1347–1361.
• Frank, P.M. (1990). Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy: A survey and some new results. automatica, 26(3), 459– 474.
• Isermann, R. (2006). Fault-diagnosis systems: an introduction from fault detection to fault tolerance. Springer Science & Business Media.
• Liu, L. and Collins, E. (2006). Robust fault detection and diagnosis for permanent magnet synchronous motors by.
• Van Overschee, P. and De Moor, B. (2012). Subspace identification for linear systems: Theory—Implementation—Applications. Springer Science & Business Media.
• Wan, Y. and Ye, H. (2012). Data-driven diagnosis of sensor precision degradation in the presence of control. Journal of Process Control, 22(1), 26–40.
• Wang, Y., Ma, G., Ding, S.X., and Li, C. (2011). Subspace aided data-driven design of robust fault detection and isolation systems. Automatica, 47(11), 2474–2480.
• Witczak, M. (2007). Modelling and estimation strategies for fault diagnosis of non-linear systems: from analytical to soft computing approaches, volume 354. Springer Science & Business Media.
• Zhang, P. and Ding, S.X. (2007). A model-free approach to fault detection of continuous-time systems based on time domain data. International Journal of Automation and Computing, 4(2), 189–194.