1. A computer implemented method for fault detection and monitoring of a dynamic system, comprising: obtaining a set of training data indicative of a normal process behavior of said dynamic system; constructing a matrix associated with a cross-correlation chart utilizing said set of training data; calculating a plurality of lags for a plurality of pair of tag variables by locating a maximum of an absolute value of a cross-correlation function in said cross-correlation chart; constructing a matrix of said plurality of lags that tracks data between each pair of tag variables among said plurality of pair of tag variables; developing a cross-covariance matrix utilizing said matrix of said plurality of lags comprising data indicative of a behavior of said dynamic system; and analyzing said cross-covariance matrix to monitor and detect faults in said dynamic system.