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

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.

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