We discuss a new diagnosis system combining wavelet analysis techniques and probabilistic classifiers for detecting tramway rollers defects. A continuous wavelet transform is applied on the vibration signals measured by specific accelerometers located on the rail. A temporal segmentation of the signals is carried out in order to identify the contribution of each pair of rollers to the overall vibration signal. The singular values decomposition (SVD) method is applied to segments of the time-scale representation to extract the most significative features. The resulting multi-class problem is then solved using pairwise classifiers trained on two-class sub-problems. The efficiency of this approach is successfully illustrated on several experiments on the tramway.
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