基于振动信号和云推理的球磨机负荷软测量

采用振动信号对球磨机料位进行测量时,特征值具有散度大、随机性强的特点.对此,基于具有将随机性、模糊性与稳定倾向性相结合能力的云模型,提出一种利用云模型对球磨机料位进行概念表示和推理测量的方法.首先,利用逆向云发生器对振动信号的功率谱特征值进行概念提取以获得前件云;然后,由料位值信息建立相对应的后件云;最后,利用云模型的不确定推理实现球磨机料位的软测量.对比实验结果表明了所提出方法的有效性和可行性.

Soft sensor for ball mill fill level based on vibration signal and cloud model reasoning

The vibration signals of ball mill bearing are found to be highly divergent and strongly stochastic when being used as a parameter of fill level. Therefore, based on the cloud model, a mathematical tool which has the property of stable tendency and the ability to organically combine the fuzziness and the randomness of the data, a method is proposed to represent the concepts of fill level and efficiently measure the fill level in ball mill. Firstly, the antecedent cloud models are obtained by using normal backward cloud generator to extract the linguistic concept from characteristic sequence generated from the power spectral density(PSD) of the vibration signals. Then the consequent clouds corresponding to the antecedent clouds are figured out by employing the fill level information of the training samples. Finally, the soft sensor of the fill level is realized by uncertainty reasoning based on the cloud model. The comparison experiments show the effectiveness and feasibility of the proposed method.