Using Optimized Time-Frequency Representations for Acoustic Quality Control of Motors

In this article a problem of industrial quality control is investigated. Electric motors have to be tested for proper working by analyzing their emitted noise signal in regular running. The noise signal being (approximately) periodic, it can be seen as a sequence of single signal segments p i (t); i = 1 These segments are represented in the time-frequency space by their time-frequency representations (TFR) C i (t; ;). For each of the two considered fault classes \beating" and \gear noise" a fault parameter is deened based on the sequence of periods. This parameter implements a coarse knowledge of the corresponding physical fault. Since the representation of the signal segments depends explicitely on the kernel function of the TFR, the ability of the fault parameter to discriminate between good and defective motors can be optimized by varying . The resulting optimum kernel functions enables a reliable classi-cation. The computational eeciency of the classiication algorithm is suu-ciently high to make possible its application in a realistic industrial production environment.