For industrial processes, one common drawback of conventional process monitoring methods is that they would make an increasing number of false alarms in cases of various factors such as catalyst deactivation, seasonal fluctuation and so forth. To address this issue, the present work proposes an online dictionary learning method, which can fulfill the process monitoring and fault diagnosis task adaptively. The proposed method would incorporate currently available information to update the dictionary and control limit, instead of keeping a fixed monitoring model. The online dictionary learning method are more superior than conventional methods. Firstly, compared with some traditional offline methods based on small amounts of historical data, the proposed method can augment train data with online dictionary updating, thus it copes with time-varying processes well. Secondly, the proposed method enjoys a lower computational complexity than the offline learning method with mass data, which is appealing in the era of industrial big data. Thirdly, the proposed method performs more reliably than the existing recursive principal component analysis-based methods because it can resolve the anomaly of principal component or non-orthogonality of eigenvectors problem which was often confronted in the recursive principal component analysis-based methods. Finally, some experiments were designed and carried out to demonstrate the advantage of the online dictionary learning.

对于工业过程，传统过程监控方法的一个共同缺点是，在催化剂失活、季节性波动等各种因素的情况下，它们会产生越来越多的误报。为了解决这个问题，目前的工作提出了一种在线字典学习方法，可以自适应地完成过程监控和故障诊断任务。所提出的方法将结合当前可用的信息来更新字典和控制限制，而不是保持固定的监控模型。在线词典学习方法比传统方法更优越。首先，与一些基于少量历史数据的传统离线方法相比，该方法可以通过在线字典更新来扩充训练数据，因此它可以很好地应对时变过程。其次，所提出的方法比具有海量数据的离线学习方法具有更低的计算复杂度，这在工业大数据时代具有吸引力。第三，所提出的方法比现有的基于递归主成分分析的方法更可靠，因为它可以解决基于递归主成分分析的方法中经常遇到的主成分异常或特征向量非正交问题。最后，设计并进行了一些实验来证明在线词典学习的优势。该方法比现有的基于递归主成分分析的方法性能更可靠，因为它可以解决基于递归主成分分析的方法中经常遇到的主成分异常或特征向量非正交问题。最后，设计并进行了一些实验来证明在线词典学习的优势。该方法比现有的基于递归主成分分析的方法性能更可靠，因为它可以解决基于递归主成分分析的方法中经常遇到的主成分异常或特征向量非正交问题。最后，设计并进行了一些实验来证明在线词典学习的优势。