- Volume 11 Issue 2
This paper deals with the problem of fault diagnosis for identifying a single fault when the number of assumed faults is larger than that of predictive variables. Principal component analysis (PCA) is employed to isolate and identify a single fault. PCA is a method to extract important information as reducing the number of large dimension in a process. The patterns of all assumed faults can be recognized by PCA and these can be employed whether a new fault is one of predefined faults or not. Through PCA, empirical models for analyzing patterns can be trained. When a single fault occurs, the pattern generated by PCA can be obtained and this is used to identify a fault. The performance of the proposed approach is illustrated in the actuator benchmark problem.
- Venkattasubramanian V., Rengaswamy R., Yin K., and Kavuri, S. N., "A Review of Process Fault Detection and Diagnosis Part 3: Quantitative Model-Based Methods", Computers and Chemical Engineering, Vol. 27, pp. 327-346, 2003. https://doi.org/10.1016/S0098-1354(02)00162-X
- Barty M., Ron Patton, Michal Syferta, Salvador de las Heras, and Joseba Quevedo, "Introduction to the DAMADICS actuator FDI benchmark study", Control Engineering Practice, Vol. 14, pp. 577-596, 2006. https://doi.org/10.1016/j.conengprac.2005.06.015
- Himmelblau, D. M., Fault Diagnosis in Chemical and Petrochemical Processes, Elsevier Press, Amsterdam, 1978. pp. 1-19.
- Chiang, L. H., Russell E. L., and Braatz, R. D., Fault Detection and Diagnosis in Industrial Systems, Springer-Verlag, London, 2001. pp. 103-169.
- Venkattasubramanian V., Rengaswamy R., Yin K., and Kavuri, S. N., "A Review of Process Fault Detection and Diagnosis Part I: Quantitative Model-Based Methods", Computers and Chemical Engineering, Vol. 27, pp. 293-311, 2003. https://doi.org/10.1016/S0098-1354(02)00160-6