A supervised functional Bayesian inference model with transfer-learning for performance enhancement of monitoring target batches with limited data

To increase the monitoring performance of the batch process with serious nonlinearity, uneven-length, and limited-data issues, a supervised transfer-learning based functional Bayesian inference method is developed in this study. The raw uneven-length batch data are firstly transformed into functional data by choosing appropriate orthogonal wavelet basis functions (WBFs). Using the approximation coefficients representing the latent features that are inherent to the raw data, a Gaussian process model and a Bayesian inference method are applied based on the coefficients in each source batch process and the established models are transferred to enhance modeling performance for the target process with limited batches. In the proposed functional method, the unfolding operation is not needed for preprocessing, avoiding distortion of the raw data structure. With the compact support property of WBFs, within-batch detection can be implemented effectively to recognize faults earlier. The advantages of the proposed model are verified using a numerical case and an industrial polytetrafluoroethylene process.

Automated summary

A supervised transfer-learning based functional Bayesian inference method is developed to improve the monitoring performance of batch processes with nonlinearity, uneven-length, and limited-data issues. The raw uneven-length batch data is transformed into functional data using orthogonal wavelet basis functions. Gaussian process models and Bayesian inference methods are applied to the latent features represented by the approximation coefficients, and the established models are transferred to enhance modeling performance for the target process with limited batches. The proposed functional method avoids distortion of the raw data structure and enables effective within-batch detection.