A feature extraction and quality grading method of acoustic signals generated from yellow poplar (liriodendron tulipifera) logs based on AR-SK

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Journal of Vibration and Shock ›› 2020, Vol. 39 ›› Issue (24) : 99-106.

XU Feng，QU Yuying

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Abstract

Aiming at the insufficient acoustic parameters in quality assessment of hardwood logs due to the non-stationary features of acoustic signals and the overlapping of defect features, a method for feature extraction and quality grading was proposed based on the autoregressive model (AR) and spectral kurtosis (SK).An AR-based linear filter was applied to filter periodic deterministic components from original signals according to the Akaike information criterion, and the residual signal containing the defect information was decomposed by the short-time Fourier transform for acquiring the SK values of sub-band components.Then, a filter was designed to filter the residual signal further for obtaining the main defect components according to the center frequency and bandwidth of the sub-band where the maximum SK value located in.Finally, the kurtosis of the filtered defect signal was used as the characteristic parameter for quality grading of the yellow poplar logs.The sawing results of the sample logs show that for the predicted log quality grade results based on AR-SK, the high-grade boards occupancy is 77.2% in the high quality log group, but that is 21.8% in the low quality group.Compared with the log quality’s predicted grade results based on the traditional velocity, the high-grade boards occupancy is increased by more than 33% in the high quality log group, while that is decreased by about 26% in the low quality log group.The results show that the proposed method can effectively separate the defect signal components from the original signal and accurately classify these hardwood logs in quality.

Key words

hardwood logs / quality assessment / kurtosis / spectral kurtosis(SK) / autoregressive mode

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XU Feng，QU Yuying. A feature extraction and quality grading method of acoustic signals generated from yellow poplar (liriodendron tulipifera) logs based on AR-SK[J]. Journal of Vibration and Shock, 2020, 39(24): 99-106

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