基于信息融合的电熔镁炉熔炼异常工况等级识别

采用多源信息融合思想，提出了电熔镁炉加热熔化异常工况等级的识别方法.在分析加热熔化过程各种异常工况的基础上，提取电流、图像和声音信号特征进行同步序列化和归一化处理，根据各种异常工况的特点选取不同特征，建立了基于支持向量机的轻微半熔化和严重过加热工况识别模型、基于规则推理的中度半熔化和严重半熔化工况识别模型和基于决策树支持向量机的轻微过加热和中度过加热工况识别模型.仿真结果表明，本文方法实现了对半熔化和过加热工况异常等级的有效识别.

Information Fusion Based Abnormal Condition Levels Recognition of Smelting in Fused Magnesium Furnace

A method of recognizing abnormal condition levels during heating and melting process of fused magnesium furnace utilizing multi-source information fusion is proposed. Current， image and sound signal features are extracted to be serialized and normalized under the premise of analyzing abnormal condition degrees during heating and melting process. Different features are selected according to the characteristics of various anomalies. The models for recognizing mild semi-melting and severe overheating conditions based on support vector machine， for moderate and severe semi-melting conditions based on rule reasoning， and for mild and moderate overheating conditions based on decision tree support vector machine， are established， respectively. The simulation results show that the proposed method can effectively recognize the abnormal condition levels of semi-melting and overheating conditions.