Ensemble learning model based on selected diverse principal component analysis models for process monitoring

Principal component analysis (PCA) is extensively applied in industrial process monitoring. For optimal performance monitoring, different faults may require different principal components (PCs). However, at present, it only selects PCs of the highest variance to create a single PCA model, thereby leading to information loss and poor monitoring performance. For the solution of this problem, a method based on ensemble learning and Bayesian inference is presented in this paper. First, numerous models are generated according to the randomly selected PCs. Next, the model with the lowest false alarm rate is retained to ensure good model performance. A novel pruning algorithm is then employed to obtain several models comprising great difference (“great difference” means the smaller similarity of the selected PCs when building different PCA models). This method enables the identification of models that can effectively predict various faults, thereby improving the monitoring performance of the ensemble model. Bayesian inference is adopted to determine the final monitoring indicator. Finally, a numerical example was used, and the Tennessee Eastman benchmark process was applied to evaluate monitoring effectiveness and illustrate the excellent performance of ensemble learning and Bayesian inference.