基于SRFML-Lift的流程制造产品质量状态监测

对流程制造型企业而言，产品质量状态的监测精度直接影响了企业的生产与运营成本。面对流程工业的多变量监测要求和数据不均衡性，以往研究主要采取局部建模策略或多输出模型，存在特征选择偏差和分类精度不高的问题。对此，本文设计了一种结合SRFML特征选择和Lift学习策略的质量状态监测模型，通过共享不同目标之间的信息以期提升模型的监测效果。首先，根据ReliefF过滤机制，引入重采样赋权思想对工业特征的选择过程进行优化(SRFML)；然后，将选择结果作为Lift学习框架的输入，通过类属属性学习方式重塑各待监测特性的特有关联属性；最后采用多个SVM分类器进行训练，得到各目标的质量状态结果。结果表明，本文构建的SRFML-Lift充分学习了原始特征的关键信息，与其他组合策略相比，对质量状态的监测效果更佳，可应用于流程工业的生产管理实践。

Process Manufacturing Product Quality Status Monitoring Based on SRFML-Lift

For process manufacturing enterprises, the monitoring accuracy of product quality status directly affects the production and operating costs of the enterprise. Facing the multi-variable monitoring requirements and data imbalance in the process industry, previous studies mainly adopted partial modeling strategies or multi-output models, which had the problems of feature selection bias and low classification accuracy. In this regard, a quality status monitoring model is designed that combines SRFML feature selection and Lift learning strategy, and is aims to improve the monitoring effect of the model by sharing information between different targets. First, according to the ReliefF filtering mechanism, the idea of resampling is introduced to optimize the selection process of industrial features (SRFML); then, the selection result is used as the input of the Lift learning framework, and the unique association of each feature to be monitored is reshaped through the generic attribute learning method Attributes; finally, multiple SVM classifiers are used for training, and the quality status results of each target are obtained. The results show that the SRFML-Lift constructed in this paper has fully learned the key information of the original characteristics, and compared with other combination strategies, it has a better monitoring effect on the quality status and can be applied to the production management practice of the process industry.