Ιδρυματικό Αποθετήριο
Πολυτεχνείο Κρήτης
Πολυτεχνείο Κρήτης
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| URI | http://purl.tuc.gr/dl/dias/2D9D3F9D-B395-49DC-91CA-C0BE70308C56 | - |
| Αναγνωριστικό | https://doi.org/10.1109/TIM.2002.1017721 | - |
| Γλώσσα | en | - |
| Μέγεθος | 12 pages | en |
| Τίτλος | Classification of washing machines vibration signals using discrete wavelet analysis for feature extraction | en |
| Δημιουργός | Stavrakakis Georgios | en |
| Δημιουργός | Σταυρακακης Γεωργιος | el |
| Δημιουργός | Zervakis Michalis | en |
| Δημιουργός | Ζερβακης Μιχαλης | el |
| Δημιουργός | S. K. Goumas | en |
| Εκδότης | Institute of Electrical and Electronics Engineers | en |
| Περίληψη | Wavelets provide a powerful tool for nonstationary signal analysis. In vibration monitoring, the occurrence of occasional transient disturbances makes the recorded signal nonstationary, especially during the start-up of an engine. Through the wavelet analysis, transients can be decomposed into a series of wavelet components, each of which is a time-domain signal that covers a specific octave frequency band. Disturbances of small extent (duration) are amplified relative to the rest of the signal when projected to similar size wavelet bases and, thus, they can be easily detected in the corresponding frequency band. This paper presents a new method for extracting features in the wavelet domain and uses them for classification of washing machines vibration transient signals. The discrete wavelet transform (DWT), in conjunction with statistical digital signal processing techniques, is used for feature extraction. The Karhunen Loeve transform (KLT) is used for feature reduction and decorrelation of the feature vectors. The Euclidean, Mahalanobis, and Bayesian distance classifiers, the learning vector quantization (LVQ) classifier, and the fuzzy gradient classifier are used for classification of the resulting feature space. Classification results are illustrated and compared for the rising part of vibration velocity signals of a variety of real washing machines with various defects | en |
| Τύπος | Peer-Reviewed Journal Publication | en |
| Τύπος | Δημοσίευση σε Περιοδικό με Κριτές | el |
| Άδεια Χρήσης | http://creativecommons.org/licenses/by/4.0/ | en |
| Ημερομηνία | 2015-10-15 | - |
| Ημερομηνία Δημοσίευσης | 2002 | - |
| Θεματική Κατηγορία | Clothing and dress--Cleaning | en |
| Θεματική Κατηγορία | Washing (Laundry) | en |
| Θεματική Κατηγορία | laundry | en |
| Θεματική Κατηγορία | clothing and dress cleaning | en |
| Θεματική Κατηγορία | washing laundry | en |
| Βιβλιογραφική Αναφορά | S. K. Goumas, M. E. Zervakis, G. S Stavrakakis, "Classification of washing machines vibration signals using discrete wavelet analysis for feature extraction,"IEEE Trans. Of Instr. and Meas., vol. 51, no. 3, pp. 497-508, Ju. 2002.doi:10.1109/TIM.2002.1017721 | en |
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