Abstract: As it can avoid pattern compounding problem of principal component analysis (PCA), designated component analysis (DCA) is introduced to implement multiple faults diagnosis of a multivariate system. A projection frame, which is the theory foundation for DCA based multiple faults diagnosis, is established in this paper. Under the DCA projection frame, anomaly detection and fault diagnosis problem is transformed into the significance detection problem for projection energy of the observation data projected to fault subspace spanned by fault patterns defined in advance. Then, for the case when designated patterns are not orthogonal to each other, a progressive DCA diagnosis method is developed by grouping the designated patterns into several orthogonal subgroups to the observation data or the residual of the previous DCA process. Simulation for data involved 5 faults shows the efficiency of the progressive DCA method for multiple faults diagnosis.