Industrial process modeling and monitoring based on jointly specific and shared dictionary learning

With the development of Industrial Cyber-Physical Systems, data of industrial processes is collected and be used for data-driven process monitoring. But due to the variation of working conditions, the data samples are always featured by characteristics and commonalities. Generally, in industrial process data, characteristics are sparse and commonalities are strongly correlated. The presence of characteristics hampers the feature extract of industrial data, thus further bringing difficulties to process monitoring. To solve this problem, this paper proposes a Jointly Specific and Shared Dictionary Learning (JSSDL) method. Specifically, we first build a specific dictionary and a shared dictionary to reconstruct characteristics and commonalities feature respectively. Then, considering the sparsity of characteristics and the strong correlation of commonalities, we add sparsity constraint to specific dictionary and low-rank constraint to shared dictionary. After that, an iterative optimization algorithm is proposed to optimize the two dictionaries. When online data samples arrive, we use the two dictionaries to reconstruct the data and determine the data is normal or faulty according to the reconstruction error. To verify the superiority of the proposed JSSDL method in process monitoring, we do extensive experiments. The experimental results demonstrate that the proposed method can achieve satisfactory monitoring results when compared with several state-of-the-art methods.