Current strategies for quality-related process monitoring mainly focus on a single quality indicator. For multiple related indicators, traditional algorithms extract the same quality-related features from variable spaces while neglecting the specific features of each indicator. Considering the correlation among these quality indicators, essential information can be captured in common features without being affected by the noise pattern of each indicator. By contrast, specific features are also needed for accuracy prediction. In this work, an end-to-end multiple quality-related model is proposed to monitor indicators jointly on the basis of a multi-task learning framework. Apart from the predictive loss of these quality indicators, this model finds the correlation among the extracted features according to the soft parameter-sharing strategy. After that, quality-related and quality-unrelated statistics are calculated to detect faults. Finally, the proposed method is evaluated by different cases in the Tennessee–Eastman and wind turbine blade icing processes.

当前用于质量相关过程监控的策略主要集中在单个质量指标上。对于多个相关指标，传统算法从变量空间提取相同的质量相关特征，而忽略每个指标的特定特征。考虑到这些质量指标之间的相关性，基本信息可以以共同的特征捕获，而不受每个指标的噪声模式的影响。相比之下，准确度预测也需要特定的功能。在这项工作中，提出了一种端到端的与质量相关的模型，以在多任务学习框架的基础上共同监视指标。除了这些质量指标的预测损失外，该模型还根据软参数共享策略在提取的特征之间找到相关性。之后，计算与质量有关和与质量无关的统计信息以检测故障。最后，在田纳西州-伊斯特曼和风力涡轮机叶片的覆冰过程中，根据不同情况对提出的方法进行了评估。