Abstract:

For overcoming the deficiency that the Baum-Welch (B-W) algorithm is easy to fall into local optimal solution, a multi-agent genetic algorithm (MAGA) is used to estimate parameters of the hidden Markov model (HMM). Chromosome coding method and genetic operation mode are designed. State monitoring and health evaluation of the temperature control amplifier are researched utilizing the state estimation and retrospect ability of the Viterbi algorithm. Only one HMM is established, which greatly reduces the calculation of model training of HMM as a categorizer. The simulation results show that the HMM optimized by MAGA has a better state monitoring performance, and it is practical to evaluate health situation of equipments using state probability obtained by the Viterbi algorithm.