MULTI-SENSOR DATA FUSION IN IDENTIFYING DIFFERENT FLOW REGIMES IN GAS-SOLID FLUIDIZED BED

Flow regime identification of gas-solid fluidized bed has been a difficult problem in fluidized field.Up to now,it is almost impossible to recognize all the flow regimes with a single sensor or parameter because of the inner complexity of the pressure signal of gas-solid phase system and the existence of transition.A new regime-division method is proposed in this paper.Fuzzy language“membership”is used to describe the degree of transition.Algorithmic complexity C(n) and fluctuation complexity Cf of the pressure signal of a seperate sensor are used as nonlinear characteristic parameters to indicate the flow regimes.Simplified models of C(n) and Cf to identify the flow regimes are established according to observation and statistics of experiments.Membership functions are given to indicate the flow regimes according to the simplified models.Data fusion at the feature level is carried out through fuzzy transformation and the identification result of a separate sensor is obtained.Data fusion at the decision level is carried out in the same way and the initial identification results are input into the decision center as local decisions.Finally the identification result of multi-parameters and multi-sensors is obtained.The experimental results show that multi-sensor data fusion can well identify the fluidized states.