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Multivariate Statistical Process Control (MSPC) approaches are now widely used for performance monitoring, fault detection and diagnosis in industrial processes. Conventional MSPC approaches are based on latent variable projection methods such as Principal Component Analysis (PCA). These methods are suitable for steady-state processes. For the systems where transient values of the processes must be taken into account, the usage of conventional PCA method causes false alarms and missing data that significantly compromise the reliability of the monitoring systems. In this paper a method is proposed to overcome false alarms which occur in the transient states according to changing process conditions and the missing data problem. The proposed monitoring method is implemented and validated experimentally on an electromechanical process. The monitoring results confirm that the proposed methodology affords credible fault detection for both the steady-state and transient operations.