基于支持向量机的纺织热轧机故障分类的研究

Research on fault classification of textile hot rolling mill based on support vector machine

纺织热轧机的故障具有多发性、隐秘性等特点,故障排查大多依赖于人工经验,如何快速定位产品故障位置,是及时维修的关键。对此,针对纺织热轧机展开了故障分类算法研究,首先基于优化的N-Gram模型对纺织热轧机故障文本进行分词处理;然后利用基于词频-逆文档频率的方法提取故障文本特征向量;最后采用遗传算法优化的支持向量机(Support Vector Machine,SVM)对故障文本建立分类模型,经实例验证了遗传算法对支持向量机分类模型优化的有效性,其分类预测精确性较高,具有良好的使用价值。

The faults of the textile hot rolling mill have had the characteristics of frequent occurrence and concealment.The troubleshooting has mostly depended on the experience of the workers.How to quickly locate the fault location of the product has been the key to timely maintenance.In this regard,the fault classification algorithm research has been carried out for the textile hot rolling mill.First,the fault text of the textile hot rolling mill has been segmented based on the optimized N-Gram model;then the fault text feature vector has been extracted by the method based on the word frequency-inverse document frequency;Finally,the support vector machine optimized by genetic algorithm has been used to establish a classification model for the fault description text.The example has verified the effectiveness of the genetic algorithm on the optimization of the support vector machine classification model.Its classification prediction accuracy has been high and model has had good use value.