Multimode Process Monitoring and Mode Identification Based on Multiple Dictionary Learning

In modern industrial systems, the processes often operate under different modes. Potential fault diagnosis and mode identification are extremely vital to maintain the system safe and reliable. Recently, many methods have been proposed to address these problems separately. Moreover, many of them make an assumption that the data from industrial site only contain the Gaussian noise. However, this assumption is not held in practice, which further reduces their performances. Considering the complicated noise feature of industrial data, we came up with an improved dictionary learning method to settle these problems simultaneously. First, the measurement data were decomposed into three parts: clean data, mode-based noise, and dense Gaussian noise. Then, the dictionary learning method was proposed to characterize each part separately. Inspired by the framework of label-consistent K-SVD, the mode information was incorporated into the dictionary learning method, and we developed a solution to settle the multiple dictionary learning optimization problem. Finally, when new samples arrive, we reconstruct them under the learned dictionary so that each sample's mode and abnormal data and can he determined. The experiments on two different types of simulated process and aluminum electrolysis process show the strength and reliability of our method, which indicates the engineering application value of the proposed method.