The Detection of Agglomeration in HSBR Based on the Analysis of an Acoustic Emission Signal

Zhang, B.; Weng, Han‐Rong

doi:10.1080/10916466.2010.531862

Cited by 2 publications

(1 citation statement)

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“…To ensure long-term stability and safe production with the FBRs, it is crucial to monitor and detect agglomeration early. Existing methods designed to detect material agglomeration faults in FBRs include the temperature method (Wildman and Huntley, 2000), electrostatic method (Xu et al, 2012), pressure uctuation method (Bartels et al, 2010), and acoustic emission method (Wang et al, 2009;Poutiainen et al, 2012;Zhang and Weng, 2012). By analyzing acoustic emission signals in FBRs, Cody et al examined the impact of particle temperature on uidized states near the walls of FBRs (Cody et al, 2000).…”

Section: Introductionmentioning

confidence: 99%

Monitoring of Agglomeration in Fluidized-Bed Reactor Based on Voiceprint Features Recognition with Unbalanced Samples

Zhao

Chen

Pang

2022

J. Chem. Eng. Japan

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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.

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