Nonlinear process monitoring using kernel dictionary learning with application to aluminum electrolysis process

In practice, because of complex mechanism processes, such as heating process, volume heterogeneity, and various chemical reaction characteristics, there is a nonlinear relationship among variables in industrial systems. The nonlinearity brings some difficulties to process monitoring. In order to ensure that the process monitoring system can work normally in nonlinear production processes, the nonlinear relationship between variables ought to be considered. In this work, a new fault detection and isolation method based on kernel dictionary learning is presented. In detail, the linearly inseparable data is mapped to a high-dimensional space. Then, a new nonlinear dictionary learning method based on kernel method was proposed to learn the dictionary. After obtaining the dictionary, the control limit can be calculated from the training data according to the kernel density estimation (KDE) method. When new data arrive, they can be represented by the well-learned dictionary, and the kernel reconstruction error can be used as a classifier for process monitoring. As for the fault data, the iterative reconstruction based method is proposed for fault isolation. In order to evaluate the effectiveness of the proposed process monitoring method, some extensive experiments on a numerical simulation, the continuous stirred tank heater (CSTH) process, and a real industrial aluminum electrolysis process are conducted. The proposed method is compared with several state-of-the-art process monitoring methods and the experimental results show that the proposed method can provide satisfactory monitoring results, especially for some small faults, thus it is suitable for process monitoring of nonlinear industrial processes.