Jarque-Bera-Based Artificial Neural Correlation Analysis for Nonlinear and Non-Gaussian Process Monitoring

Nonlinear and non-Gaussian characteristics are common in industrial processes. Artificial neural correlation analysis (ANCA) is a good nonlinear process monitoring algorithm, which combines classical correlation analysis with artificial neural networks. However, its performance is not very satisfactory for industrial processes with non-Gaussian characteristics. To solve non-Gaussian problems, almost all the existing process monitoring algorithms only consider the effect of kurtosis. Nevertheless, both kurtosis and skewness affect the data distribution. To improve the limitations of existing algorithms, this study proposes a new process monitoring algorithm named Jarque-Bera-based ANCA. This new algorithm makes many improvements to ANCA scheme, and the designed loss function combines the influence of both kurtosis and skewness on the data distribution, which not only maintains the advantages of the ANCA algorithm in solving nonlinear problems, but also provides superior monitoring performance in non-Gaussian processes. Furthermore, the superior performance of the proposed new algorithm is verified through simulations using non-Gaussian and nonlinear numerical examples, the Tennessee Eastman process, and catalytic cracking units.