Abstract：Wavelet packet analysis method was used to study the extraction of splash prediction for AOD furnace from audio signal of furnace opening. Using db10 wavelet completed 4 layers wavelet packet decomposition from characteristic signal before splash, combined with fast Fourier transform method and wavelet scale spectrum to analyse the time-frequency characteristic, the energy ratio characteristics of the signal frequency band decomposition. The test results show that the frequency value of the 40s signal before splash was significantly lower than normal signal, the signal energy ratio between 0-312Hz and 312-625Hz frequency band changed significantly. Additionally, the low frequency reconstruction signal was excellent in filtering a variety of scene interference signal, which shows that the time-frequency characteristics can forecast splash feature vector. Finally, 8 feature vectors was determined by the experiment and compared with the feature vector of splash or normal signals, and verified that relevance 0.95 can be used as a threshold of splash forecast decision. So that realizing the accurate feature extraction of splash prediction; the prediction result can be converted into computer numerical characteristics easily recognized.
李岩，吴立斌，尤文. 基于小波包分析的AOD炉喷溅预报特征信息提取[J]. 中国冶金, 2014, 24(12): 12-18.
LI Yan，WU Li-bin，YOU Wen. Feature extraction of splash prediction for AOD furnace based on wavelet packet analysis. China Metallurgy, 2014, 24(12): 12-18.
2014, Vol. 24 
