Abstract: A root cause diagnosis model of head narrowing in hot rolled strip was proposed based on the dissimilarity index and comparative Granger causality analysis. Firstly, kernel entropy component analysis (KECA) was used to extract the feature of the original data, and  the dissimilarity evaluation was realized based on the dimensionality reduction under the nonlinear relationship. Secondly, comparative Granger causality analysis model was established. Therefore, the allowable fluctuation range of each causal relationship was calculated by batch data under a large number of normal conditions, which was regarded as a criterion corresponding to judge whether the causal relationship was abnormal. The final root cause diagnosis of the abnormal was determined by the causal relationships. Finally, the diagnosis model of the head narrowing in hot rolling was established to verify the effectiveness of the method using real industrial data. Results show that the method has better performance, which may accurately detect abnormal batches, and locate root cause of abnormal batches.

Key words: root cause analysis, nonlinear dissimilarity analysis, comparative Granger causality analysis, hot rolled strip, head narrowing

摘要： 提出了基于相异度指标和对比格兰杰因果关系分析的热轧带钢头部拉窄根因诊断模型。采用核熵成分分析对原始数据进行特征提取，在降维基础上实现非线性关系下的相异度评估，判定生产状况是否异常；建立对比格兰杰因果关系模型，通过大量正常工况下的批次数据得到各因果关系的允许波动范围，并作为对应因果关系是否异常的标准，进而确定最终故障根因；最后，利用大量实际生产数据建立热轧带钢头部拉窄根因诊断模型，验证了方法的有效性。结果表明，该方法具有较好的性能，能够准确检测出异常批次并定位根因。

关键词: 根因分析, 非线性相异度分析, 对比格兰杰因果关系分析, 热轧带钢, 头部拉窄