Soft sensor for ball mill fill level based on uncertainty reasoning of cloud model

Considering the strong uncertainty in ball mills, cloud model which combines fuzziness and randomness together and has the ability of processing uncertainty, is introduced. The paper proposes a novel soft sensor based on uncertainty reasoning of cloud model to improve the accuracy and reliability of fill level measurement. At first, power spectral densities of the vibration signals are extracted by Welch's method and the features are obtained by summing the energy of a wide frequency band. Then backward cloud generator algorithm is used to represent numerical characteristics of antecedent clouds under different fill levels, and the corresponding consequent clouds are given according to the fill level information. Thus, the rule base is built and the uncertainty reasoning based on cloud model is realized. As it is difficult to obtain data set continuously and accurately in practical industry fields, virtual cloud is employed to deal with the problem of sparse rule base in the case of insufficient samples. The experimental results show that the accuracy of proposed method can meet the requirements of field measurement applications. In addition, the method based on virtual cloud is more accurate and robust compared with other methods in the case of insufficient samples.