In order to solve the problems of complicated fault diagnosis and poor fault diagnosis of vertical mill operation, this paper proposes a diagnostic method based on fisher and information entropy difference classification. By extracting the fault feature of the anomaly attribute–the maximum value of the attribute, and the possible faults can be determined according to the fault characteristics. Then the information entropy of each sample is calculated, and the entropy difference between normal and fault states is calculated. The normal and fault conditions can be classified by fisher classifier. This method can capture the instantaneous change of the fault and detect the moment when the fault occurs. And the effectiveness of the feature extraction method is verified by experiments.

针对立磨运行故障诊断复杂、故障诊断不力的问题，提出一种基于Fisher和信息熵差分分类的诊断方法。通过提取异常属性的故障特征——属性的最大值，可以根据故障特征确定可能的故障。然后计算每个样本的信息熵，计算正常状态和故障状态的熵差。正常和故障条件可以通过 Fisher 分类器进行分类。该方法可以捕捉故障的瞬时变化，检测故障发生的时刻。并通过实验验证了特征提取方法的有效性。