This paper proposes a cross-channel image chain scrambling encryption algorithm based on two 5D memristive twin multi-scroll hyperchaotic systems. Firstly, two flux-controlled memristor models are introduced based on the simplification of Chua's system, and two 5D memristive twin multi-scroll hyperchaotic systems are designed by inputting different flux variables. The twin 5D memristive multi-scroll hyperchaotic systems exhibit the characteristics of variable scroll numbers and different topological structures. Secondly, a fractal-like cross-channel encryption algorithm is proposed, which combines cross-channel scrambling of sub-image and Zigzag transformation. Based on comprehensive experimental analysis, the results reveal that the key space is determined to be 2 525 , the information entropy of the ciphertext images across three channels approaches 7.9994, and the pixel correlation is reduced to nearly zero. Furthermore, NPCR and UACI exhibit values close to their theoretical benchmarks of 99.6094 % and 33.4635 %, respectively, highlighting the superior performance of the proposed encryption algorithm in terms of robustness and security.

ABSTRACT Industrial processes are specialized and intricate systems. Current intelligent fault diagnosis methods do not take into account the interactions between individual units and variables, instead using only the temporal or Euclidean geometric space characteristics of industrial process data. How to utilize the complex relationship between variables for fault diagnosis remains an issue to be solved. This study proposed a fault diagnosis framework based on the deep spatiotemporal fusion graph convolutional network (DSTFGCN) for graph representation learning of correlations between variables. First, the maximum information coefficient was introduced to represent the complex correlation between variables in the graph signal construction process. Second, to effectively extract spatiotemporal features from the data, the graph convolutional network (GCN) and the convolutional neural network (CNN) were introduced into the DSTFGCN for mining complex spatial features in the data, and the long short‐term memory (LSTM) network was employed to capture the evolution of multivariate time series. Consequently, the fault detection and false‐positive rates of the proposed model were, respectively, 94.45% and 0.22% in the Tennessee Eastman Process (TEP), whereas the rates were, respectively, 99.61% and 0.07% on the Three‐Phase Flow Facility (TPFF) datasets. These experimental results demonstrate the excellent performance and robustness of the proposed model, compared to those of both machine learning and deep learning models.