Research on Statistical Modeling of Process Data via Wavelet Domain Hidden Markov Model

A wavelet and hidden Markov model (HMM) based approach is introduced to build the statistical model of process data. Wavelet transform provides a compact, information-rich expression of process data through a set of coefficients that carry localized transient information of process operating condition. The non-Gaussian properties of process data are characterized by a mixture Gaussian distribution. And the serial correlations in the data are described by the state transition of hidden Markov model. Case studies from CSTR illustrate that the inherent characteristics of process data can be accurately modeled by wavelet and HMM.