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HMM-Based Transient Identification in Dynamic Process

Kee Choon Kwon
Transaction on Control Automation, and Systems Engineering, vol. 2, no. 1, pp.40-46, 2000

Abstract : In this paper, a transient identification based on a Hidden Markov (HMM) has been suggested and evaluated experimentally for the classification of transients in the dynamic process, The transient can be identified by its unique time dependent patterns related to the principal variables. The HMM, a double stochastic process, can be applied to transient identification which is a spatial and temporal classification problem under a statistical pattern recognition framework. The HMM is created for each transient form a set of training data by the manimum-likelihood estimation method. The transient identification is determined by calculation which modelhas the highest probability for the given test data. Several experimental tests have been performed with normalization methods, clustering algorithms, and a number of states in HMM s everal experimental tests have been performed including superimposing random noise, adding systematic error, and untrained transients. The proposed real-time tracsient identification system has many advantages, however, there are still a lot of problems that should be solved to apply to a real dynamic process. Further efforts are being made to improce the system performance and robustness to demonstrate reliability and accuracy the required level.

Keyword : transient identification, hidden Markov model, statistical pattern recognition

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