Process monitoring, a typical application of industrial Internet of Things (IIOT), is crucial to ensure the reliable operation of the industrial system. In practice, due to the harsh environment and unreliable sensors and actuators, it is often difficult for IIoT to collect enough tagged and highly reliable data, which further degrades the process monitoring performance and makes the monitoring results not trustworthy. In order to reduce the negative impact of these unreliable factors, a self-weighted dictionary learning process monitoring method is proposed. In particular, a label propagation classifier is implemented from the labeled data to unlabeled data to obtain a credible label prediction. Subsequently, considering the interference of low-quality data and label information, we reweight the classification loss and label-consistency constraints to enhance the trustworthiness of feature extraction. Finally, a novel iterative optimization algorithm that combines the block coordinate descent method with the alternating direction multiplier method is developed to ensure the convergence speed of the learned classifier and dictionary. Extensive experiments indicate that the proposed method can guarantee the trustworthiness of the process monitoring results.

中文翻译：

---

基于自加权字典学习的工业物联网过程监控可信度


过程监控是工业物联网的典型应用，对于保证工业系统的可靠运行至关重要。在实践中，由于恶劣的环境以及不可靠的传感器和执行器，工业物联网往往很难收集足够的标记且高可靠的数据，这进一步降低了过程监控性能，使得监控结果不可信。为了减少这些不可靠因素的负面影响，提出了一种自加权字典学习过程监控方法。特别是，从标记数据到未标记数据实现标签传播分类器，以获得可信的标签预测。随后，考虑到低质量数据和标签信息的干扰，我们重新加权分类损失和标签一致性约束，以增强特征提取的可信度。最后，开发了一种新颖的迭代优化算法，将块坐标下降法与交替方向乘子法相结合，以确保学习到的分类器和字典的收敛速度。大量实验表明，该方法能够保证过程监测结果的可信度。