When acoustic emission detection technology is applied to detect the agglomeration in uidized bed reactors (FBRs), the collected acoustic emission samples are usually non-stationarity and unbalanced, making it di cult to extract stable and separable classi cation features. In this study, the voiceprint features of collected acoustic emission signals were extracted with the Mel Frequency Cepstrum Coe cients (MFCC) and Linear Prediction Cepstrum Coe cients (LPCC). Extracted voiceprint features of LPCC and MFCC were fused with RelieF algorithm to form the stable R-LPMFCC feature, which were then compressed with principal components analysis (PCA) as input data for classi cation. The cost factor and GINI index-based decision-making calculation were introduced to the Adaboost algorithm to signi cantly improve its accuracy and F-score when classifying unbalanced samples. The comparative experimental results in a uidized-bed pilot plant verify the e ectiveness and feasibility of the proposed method.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.