Intelligent online quality control using discrete wavelet analysis features and likelihood classification

S. Goumas; M. Zervakis; A. Pouliezos; G. S. Stavrakakis; Enrico Primo Tomasini; Nicola Paone; Lorenzo Scalise

doi:10.1117/12.386766

22 May 2000 Intelligent online quality control using discrete wavelet analysis features and likelihood classification

S. Goumas, M. Zervakis, A. Pouliezos, G. S. Stavrakakis, Enrico Primo Tomasini, Nicola Paone, Lorenzo Scalise

Proceedings Volume 4072, Fourth International Conference on Vibration Measurements by Laser Techniques: Advances and Applications; (2000) https://doi.org/10.1117/12.386766
Event: 4th International Conference on Vibration Measurement by Laser Techniques, 2000, Ancona, Italy

Abstract

This paper presents a method for extracting features in the wavelet domain of vibration velocity transient signals of washing machines, that are then used for classification of the state (acceptable-faulty) of the product. The Discrete Wavelet Transform in conjunction with Statistical Digital Signal Processing techniques are used for feature extraction. The performance of this feature set is compared to features obtained through standard Fourier analysis of the stationary part of the signal. Minimum distance Bayes classifiers are used for classification purposes. Measurements from a variety of defective/non-defective washing machines taken in the laboratory as well as from the production line are used to illustrate the applicability of the proposed method.

Citation Download Citation

S. Goumas, M. Zervakis, A. Pouliezos, G. S. Stavrakakis, Enrico Primo Tomasini, Nicola Paone, and Lorenzo Scalise "Intelligent online quality control using discrete wavelet analysis features and likelihood classification", Proc. SPIE 4072, Fourth International Conference on Vibration Measurements by Laser Techniques: Advances and Applications, (22 May 2000); https://doi.org/10.1117/12.386766

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