Reduction of detectable fault size in sampled data residuals

https://doi.org/10.58571/CNCA.AMCA.2024.070

Resumen: One of the families of robust algorithms for fault detection in dynamic systems is related to the use of mathematical models. This poses challenges when access to the mathematical model for design is not available. This work employs a scheme capable of diagnosing systems based on input-output data (designed under nominal operation and the assumption that the system exhibits essentially linear behavior). A relevant aspect of diagnosis is the sensitivity to the faults being detected. This sensitivity plays a crucial role in critical systems, where early fault detection is desired. This work offers an approach capable of adjusting the diagnostic sensitivity of the residual-generating algorithms with respect to faults. The idea is to obtain residuals that are triggered once a certain predetermined threshold value is exceeded due to the effect of faults. Based on an application example, it is illustrated that the proposed residual generator is approximately 20 times more sensitive than those reported in the literature.

¿Cómo citar?

Lozano Flores, H.A., Alcorta García, E. & Perez Rojas, C. (2024). Reduction of detectable fault size in sampled data residuals. Memorias del Congreso Nacional de Control Automático 2024, pp. 410-415. https://doi.org/10.58571/CNCA.AMCA.2024.070

Palabras clave

Linear systems, sampled data, observers, residuals, faults, sensitivity

• Cai, P. and Deng, X. (2020). Incipient fault detection for nonlinear processes based on dynamic multiblock probability related kernel principal component analysis. ISA Transactions, 105, 210–220. doi: 10.1016/j.isatra.2020.05.029.
• Chen, J. (1999). Robust Model-Based Fault Diagnosis for Dynamic Systems. Springer US. Escobet, T., Puig, V., Quevedo, J., and Garcia, D. (2014). A methodology for incipient fault detection. In 2014 IEEE Conference on Control Applications (CCA). IEEE. doi:10.1109/cca.2014.6981336.
• Frank, P.M. (1990). Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy. Automatica, 26(3), 459–474. doi:10.1016/0005-1098(90)90018-d.
• Liu, Q., Liu, S., Dai, Q., Yu, X., Teng, D., and Wei, M. (2021). Data-driven approaches for diagnosis of incipient faults in cutting arms of the roadheader. Shock and Vibration, 2021, 1–15. doi:10.1155/2021/8865068.
• Safaeipour, H., Forouzanfar, M., and Casavola, A. (2021). A survey and classification of incipient fault diagnosis approaches. Journal of Process Control, 97, 1–16. doi: 10.1016/j.jprocont.2020.11.005.
• Sanchez, L., Alcorta, E., and Leal, I. (2018). Enfoque para aislamiento de fallas entiempo continuo a partir de datos muestreados. Memorias del Congreso Nacional de Control Automático.
• Vishwanath, R., Shetty, V.A., Poonam, A., Shamilli, S., and Thanuja, M. (2015). A new approach to monitor condition of transformers incipient fault diagnosis based on gsm & xbee. International Journal of Engineering Development and Research, 3(2), 875–882.
• 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. doi:10.1007/s11633-007-0189-y.
• Zhang, X., Delpha, C., and Diallo, D. (2020). Incipient fault detection and estimation based onjensen–shannon divergence in a data-driven approach. Signal Processing, 169, 107410. doi: 10.1016/j.sigpro.2019.107410.