Shijun Tao’s research while affiliated with Central South University and other places

With the development of information technologies, a large number of sensors have been deployed to obtain industrial data. As a result, data-driven approaches have become a crucial means for modeling of distributed parameter systems (DPS). However, due to harsh environments and unreliable sensors, data is often of low quality in practice, which in turn poses a challenge for data-driven modeling approaches. In order to address the challenge of inaccurate modeling of DPS induced by outliers, this paper proposes a physical-informed sparse learning method to overcome the adverse effects of outliers and achieve robust modeling of DPS by fully exploring the spatiotemporal dynamic of DPS. Specifically, this paper proposes an innovative method for robust modeling of DPS. The method incorporates the statistical features of contaminated data to restore the dynamic evolution structure of DPS, which weakens the adverse effects of outliers and address the problem of low modeling accuracy caused by contaminated observation data. Furthermore, the underlying partial differential equation (PDE) of DPS is incorporated into the constraint on the temporal deviation data, which leads to a physical-informed optimization objective and improves the reliability of outlier extraction and DPS modeling. Finally, an optimization algorithm based on the alternating direction method of multipliers (ADMM) with an adaptive penalty factor is proposed. This ensures the convergence of multivariate optimization problem and superior performance of the DPS modeling framework. Extensive experimental results have verified that the proposed method is effective in overcoming the adverse effects of outliers and achieving robust modeling of DPS. Note to Practitioners —The motivation of this paper is to develop an interpretable and robust modeling method for DPS. Considering the negative impact of outliers, the proposed method first restores the dynamic properties of the data based on the characteristics of the outliers. Then, the embedding of physical knowledge ensures the reliability of outlier removal and robustness of modeling. Extensive experimental results have verified that the proposed method can effectively overcome the adverse effects of outliers and outperform some state-of-the-art methods. Therefore, it is more suitable for real industrial systems.

With the development of the industrial cyber-physical systems, a small amount of labeled data and a large amount of unlabeled data are collected from the industrial process. Due to the variation of internal operation conditions and external environment, there is a between-mode similarity between data samples. The scarcity of labeled data and the existence of similarity make it challenging to extract data characteristics. In addition, it creates new challenges to process monitoring. To solve these problems, this study proposes a label propagation dictionary learning method. We first establish the connection between atoms and corresponding profiles and realize the propagation of their labels through graph Laplacian regularization. Then, considering the similarity of samples in the same class, the low-rank constraint is added to sparse coding to strengthen the mutual propagation of labels. Finally, an optimization method is designed to obtain the dictionary and classifier simultaneously. When new data samples arrive, we conduct process monitoring and condition prediction based on the learned dictionary and classifier. Experiments show that the proposed method can achieve satisfactory monitoring performance when compared to several state-of-the-art methods, indicating the superiority of the proposed method.

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 re-weight 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.