基于软测量和数据融合的煤与瓦斯突出预测

Prediction of coal and gas outbursts based on soft sensor and data fusion technologies

文章针对现有煤与瓦斯突出预测方法存在的不足,提出了一种基于软测量和数据融合技术的煤与瓦斯突出危险状况预测方法;利用检测到的煤与瓦斯突出的多种影响因素数据,建立了基于模糊BP神经网络的软测量模型对煤与瓦斯突出危险状况进行动态和准确地预测,并应用基于均值的分批估计融合方法对检测到的因素数据进行处理,提高数据检测的精度,进一步增强煤与瓦斯突出危险状况预测的准确性;通过实例对方法进行验证,结果表明,提出的方法预测准确性高,是一种有效的煤与瓦斯突出预测方法。

In view of the problems existing in the current prediction methods of coal and gas outbursts, a prediction method based on soft sensor and data fusion technologies is proposed. The measured data relevant to multiple influencing factors are taken as inputs, and the soft sensor model for dynamic and accurate prediction of coal and gas outbursts is given based on fuzzy BP ANN. And the data fusion method based on the arithmetic mean and batch estimation is used to filter the collected data so as to improve the precision of data collection and the accuracy of prediction. Practical examples are presented,and the result shows that the proposed method has high accuracy and it is a practical method to predict coal and gas outbursts.