New Nonlinear Approach for Process Monitoring: Neural Component Analysis

Nonlinearity is extremely common in industrial processes. For handling the nonlinearity problem, this paper combines artificial neural networks (ANN) with principal component analysis (PCA) and proposes a new neural component analysis (NCA). NCA has a similar network structure as ANN and adopts the gradient descent method for training, hence it has the same nonlinear fitting ability as ANN. Furthermore, NCA adopts PCA's dimension reduction strategy to extract the uncorrelated components from the process data and constructs statistical indices for process monitoring. The simulation test results show that NCA can successfully extract the uncorrelated components from the nonlinear process data, and it has better performance than other nonlinear approaches.

Automated summary

The proposed Neural Component Analysis (NCA) combines artificial neural networks (ANN) with principal component analysis (PCA) to address the common nonlinearity in industrial processes. NCA, with a similar network structure as ANN and utilizing the gradient descent method for training, successfully extracts uncorrelated components from the process data with PCA's dimension reduction strategy, and constructs statistical indices for process monitoring, showing superior performance compared to other nonlinear approaches in simulation tests.