N4SID: subspace algorithms for the identification of combined deterministic-stochastic systems
Automatica (Journal of IFAC) - Special issue on statistical signal processing and control
A unifying theorem for three subspace system identification algorithms
Automatica (Journal of IFAC) - Special issue on trends in system identification
Subspace algorithms for the identification of multivarible dynamic errors-in-variables models
Automatica (Journal of IFAC)
On fault detection in linear discrete-time, periodic, and sampled-data systems
Journal of Control Science and Engineering - Robustness Issues in Fault Diagnosis and Fault Tolerant Control
Least-squares based iterative parameter estimation for two-input multirate sampled-data systems
ACC'09 Proceedings of the 2009 conference on American Control Conference
Recursive least squares identification for multirate multi-input single-output systems
ACC'09 Proceedings of the 2009 conference on American Control Conference
Auxiliary model identification method for multirate multi-input systems based on least squares
Mathematical and Computer Modelling: An International Journal
Mathematical and Computer Modelling: An International Journal
Hi-index | 22.14 |
This paper proposes a novel subspace approach towards direct identification of a residual model for fault detection and isolation (FDI) in a system with non-uniformly sampled multirate (NUSM) data without any knowledge of the system. From the identified residual model, an optimal primary residual vector (PRV) is generated for fault detection. Furthermore, by transforming the PRV into a set of structured residual vectors, fault isolation is performed. The proposed algorithms have been applied to an experimental pilot plant with NUSM data for sensor FDI, where different types of faults are successfully detected and isolated, fully validating the practicality and utility of the developed theory.