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A fault detection approach based on the combination of the Generalized Consistency Check and the Sequential Probability Ratio Test is developed and applied for validation of signals from process sensors. The basic methodology requires at least triple redundancy of a given measurement from like sensors and analytical measurements. The separate measurement of the signal mean value and the random fluctuation improves the reliability of fault identification and signal reconstruction. The diagnostics of the source of anomaly in a sub-system is performed by multivariate autoregressive modeling of the process signals and the analysis of resulting signatures.