Abstract

EXTENDED ABSTRACT This paper proposes an efficient online steady state detection method for multivariate systems through a sequential Bayesian partitioning approach. The signal is modelled by a Bayesian piecewise constant mean and covariance model, and a recursive updating method is developed to calculate the posterior distributions analytically. The duration of the current segment is utilized for steady state testing. Insightful guidance is provided for hyperparameter selection. The effectiveness of the proposed method is demonstrated through thorough numeric and real case studies.

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