Compared with the normal process, the statistical values or the relationships between process variables and the quality that represent process situations would change in an abnormal batch. A novel method named quality relevant fault detection based on statistical pattern and regression coefficients (SPRC) is proposed for the batch process. Firstly, the statistical patterns of the process data, such as mean value and SD, are computed to quantify process characteristics. The regression model is built via linear methods, such as multiple linear regression, least absolute shrinkage and selection operator, to describe the linear relationship. The mutual information between the quality and the process parameters is used to characterize the nonlinear relationship. In this way, statistical patterns and regression coefficients which express batch information constitute the two‐way matrix. Then, the matrix is dimensionally reduced by related method, such as principal component analysis. The relationships hidden in batch process could be sought by SPRC. Finally, two cases, penicillin fermentation and steel hot rolling, are used to validate the feasibility and advantages of the proposed method.

中文翻译：

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基于统计模式和回归系数的批处理过程中与质量相关的故障检测

与正常过程相比，异常批次中的统计值或代表过程状况的质量与过程变量之间的关系会发生变化。提出了一种基于统计模式和回归系数（SPRC）的质量相关故障检测新方法。首先，计算过程数据的统计模式，例如平均值和SD，以量化过程特性。通过线性方法（例如多元线性回归，最小绝对收缩和选择算子）构建回归模型，以描述线性关系。质量和过程参数之间的相互信息用于表征非线性关系。这样，表示批处理信息的统计模式和回归系数构成了双向矩阵。然后，通过相关方法（例如主成分分析）对矩阵进行降维处理。SPRC可以寻求隐藏在批处理过程中的关系。最后，以青霉素发酵和钢热轧两种情况验证了该方法的可行性和优势。