Generalized likelihood ratio method for gross error identification

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Abstract
A new method for detecting, identifying, and estimating gross errors in steady state processes is described in this paper. The generalized likelihood ratio method is based on the likelihood ratio statistical test and provides a general framework for identifying any type of gross error that can be modeled. The procedure is illustrated with gross errors caused by measurement biases and leaks. One significant advantage of the method is that the identification of gross errors is not confounded by departure from steady state conditions, which may now be accounted for by “leaks”. Also proposed is a new strategy for identifying multiple gross errors using serial compensation of gross errors, which may be applied to all types of gross errors including leaks and biases and which requires less computing time than the existing strategies.