Browsing by Subject "Acoustic emissions"

Now showing 1 - 2 of 2

Open Access
Feasibility of impact-acoustic emissions for detection of damaged wheat kernels
(Elsevier BV, 2007-05) Pearson, T.; Çetin, A. Enis; Tewfik, A. H.; Haff, R. P.
A non-destructive, real time device was developed to detect insect damage, sprout damage, and scab damage in kernels of wheat. Kernels are impacted onto a steel plate and the resulting acoustic signal analyzed to detect damage. The acoustic signal was processed using four different methods: modeling of the signal in the time-domain, computing time-domain signal variances and maximums in short-time windows, analysis of the frequency spectrum magnitudes, and analysis of a derivative spectrum. Features were used as inputs to a stepwise discriminant analysis routine, which selected a small subset of features for accurate classification using a neural network. For a network presented with only insect damaged kernels (IDK) with exit holes and undamaged kernels, 87% of the former and 98% of the latter were correctly classified. It was also possible to distinguish undamaged, IDK, sprout-damaged, and scab-damaged kernels.
Open Access
Wheat and hazelnut inspection with impact acoustics time-frequency patterns
(ASABE, 2007-06) İnce, N. F.; Onaran, İbrahim; Tewfik, A. H.; Kalkan, H.; Pearson, T.; Çetin, A. Enis; Yardimci, Y.
Kernel damage caused by insects and fungi is one of the most common reason for poor flour quality. Cracked hazelnut shells are prone to infection by cancer producing mold. We propose a new adaptive time-frequency classification procedure for detecting cracked hazelnut shells and damaged wheat kernels using impact acoustic emissions recorded by dropping wheat kernels or hazelnut shells on a steel plate. The proposed algorithm is based on a flexible local discriminant bases (F-LDB) procedure. The F-LDB method combines local cosine packet analysis and a frequency axis clustering approach which supports individual time and frequency band adaptation. Discriminant features are extracted from the adaptively segmented acoustic signal, sorted according to a Fisher class separability criterion, post processed by principal component analysis and fed to linear discriminant. We describe experimental results that establish the superior performance of the proposed approach when compared with prior techniques reported in the literature or used in the field. Our approach achieved classification accuracy in paired separation of undamaged wheat kernels from IDK, Pupae and Scab damaged kernels with 96%, 82% and 94%. For hazelnuts the accuracy was 97.1%.