Intelligent on-line quality control of washing machines using discrete wavelet analysis features and likelihood classification

This paper presents a method for extracting features in the wavelet domain from the vibration velocity signals of washing machines, focusing on the transient (non-stationary) part of the signal. These features are then used for classification of the state (acceptable-faulty) of the machine. The performance of this feature set is compared to features obtained through standard Fourier analysis of the steady-state (stationary) part of the vibration 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. (C) 2002 Published by Elsevier Science Ltd.