A new subspace identification approach based on principal component analysis

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A Review of Fault Detection and Diagnosis for the Traction System in High-Speed Trains (2020, background): “where δkj denotes the Kronecker delta function [50].”
An overview of subspace identification (2006, methodology): “Wang and Qin 2002) proposed the use of parity space and principal component nalysis (PCA) for EIV and closed-loop identification which is pplicable to correlated input excitation.”
Subspace identification for fractional order Hammerstein systems based on instrumental variables (2012, background): “Pre-multipying (18) with ( ) , T i ⊥ Γ the orthogonal complement of Γi with full column rank [15], and moving the input matrix term, (18) becomes”
Closed-loop subspace identification: an orthogonal projection approach (2004, methodology): “Many methods are available for this purpose, for example, (Wang and Qin, 2002).”
A Review on Basic Data-Driven Approaches for Industrial Process Monitoring (2014, background): “Recently, based on the intimate relationship between the wellestablished SIM and parity vectors [27], [74], [75], a new model-free data-driven design procedure is proposed in [76], whose core is a residual generator in the form of the diagnostic observer (DO) that is directly constructed using the p”