Due to technological advancements, many manufacturing processes are now real-time monitored through sensors that provide continuous signals of the process parameters rather than providing simpler point observations of the process response. Signals (profiles) obtained through these sensors can reveal important information about the quality of the process being monitored. In this work, we propose a general predictive control framework for on-line process quality monitoring where data is available in the form of a profile. The proposed framework is an integration of ideas from classical on-line process control and advanced machine learning techniques, namely, Random Forests. The proposed framework has the advantages of being more interpretable compared to other methods found in the literature, and has the flexibility to include several commonly used transformations of the signal as features. In addition, abnormal out of control signal characteristics of the process known from experience by operators can be easily incorporated in the random forest technique. An illustration of the proposed framework applied to the case of a tapping manufacturing process is provided. Model comparison results show a superior performance of the proposed framework over previously proposed monitoring methods for the considered tapping process. From a receiver operating characteristic curve analysis, it was found that an area under the curve (AUC) of 0.923 was achieved by the proposed model compared to an AUC of 0.867 for the Generalized Variance model proposed in the literature.

由于技术的进步，现在许多制造过程都通过传感器进行实时监控，这些传感器提供过程参数的连续信号，而不是提供对过程响应的简单点观察。通过这些传感器获得的信号（配置文件）可以揭示有关所监视过程质量的重要信息。在这项工作中，我们提出了一个在线过程质量监控的通用预测控制框架，其中数据以配置文件的形式可用。提出的框架是经典在线过程控制和高级机器学习技术（即随机森林）的思想的整合。与文献中发现的其他方法相比，提出的框架具有更易于解释的优势，并且可以灵活地将信号的几种常用转换作为特征。另外，从操作员的经验中获知的过程的异常失控信号特征可以容易地并入随机森林技术中。提供了适用于攻丝制造工艺情况的建议框架的说明。模型比较结果表明，对于考虑的攻丝过程，所提出的框架优于先前提出的监视方法。从接收机工作特性曲线分析中发现，与文献中提出的广义方差模型的AUC为0.867相比，所提出的模型可实现曲线下面积（AUC）为0.923。操作员从经验中获知的过程的异常失控信号特征可以轻松地纳入随机森林技术中。提供了适用于攻丝制造工艺情况的拟议框架的说明。模型比较结果表明，对于考虑的攻丝过程，该框架的性能优于先前提出的监视方法。从接收机工作特性曲线分析中发现，与文献中提出的广义方差模型的AUC为0.867相比，所提出的模型可实现曲线下面积（AUC）为0.923。操作员从经验中获知的过程的异常失控信号特征可以轻松地纳入随机森林技术中。提供了适用于攻丝制造工艺情况的建议框架的说明。模型比较结果表明，对于考虑的攻丝过程，该框架的性能优于先前提出的监视方法。从接收机工作特性曲线分析中发现，与文献中提出的广义方差模型的AUC为0.867相比，所提出的模型可实现曲线下面积（AUC）为0.923。提供了适用于攻丝制造工艺情况的建议框架的说明。模型比较结果表明，对于考虑的攻丝过程，所提出的框架优于先前提出的监视方法。从接收机工作特性曲线分析中发现，与文献中提出的广义方差模型的AUC为0.867相比，所提出的模型可实现曲线下面积（AUC）为0.923。提供了适用于攻丝制造工艺情况的建议框架的说明。模型比较结果表明，对于考虑的攻丝过程，所提出的框架优于先前提出的监视方法。从接收机工作特性曲线分析中发现，与文献中提出的广义方差模型的AUC为0.867相比，所提出的模型可实现曲线下面积（AUC）为0.923。