Recursive identification algorithm for parity space based fault detection systems

The problem of recursively identifying parity space in the framework of subspace technique is studied. Updating the entire singular value decomposition, a crucial step in identification, is computationally burdensome and sometimes not even feasible. Hence a recursive eigenvalue decomposition based identification method is recommended in the literature. The algorithm developed here updates the eigenstructure of covariance matrix of input and output data after every new measurement and gives a new parity space. The method improves the fault detection performance against uncertain parameter variations and in non-stationary noise environment. The proposed algorithm is applied to hybrid simulation platform of continuous stirred tank reactor.