JIANG Wan-lu1,2, LEI Ya-fei1,2, HAN Ke1,2,3, ZHANG Sheng1,2, SU Xiao1,2
JOURNAL OF VIBRATION AND SHOCK. 2018, 37(22): 43-50.
A performance degradation degree quantitative assessment method for rolling bearings was proposed, which integrates the methods of Variational Mode Decomposition(VMD) and Support Vector Data Description (SVDD).Aiming at solving the problem that some parts of a signal may be disturbed by abnormal signals if the sampling time is long and the collected signal contains too many data points.A new feature extraction method based on VMD and SVDD was proposed, in which the long signal was segmented into several short frame signals, and the short signals were decomposed by VMD to obtain several components.The singular value of each component was extracted respectively to form a feature vector, and then a set of feature vectors was obtained.After finding and removing outliers by SVDD, the average value of the remained feature vectors was used as the feature of the original signal.Following the feature extraction, SVDD was used to assess the performance degradation.The degree of performance degradation was described by the distance from the test sample to the center of the hypersphere model, and the membership function was used to transform the distance index into the membership degree to the normal state and taken as the performance degradation index, which quantitatively assesses the performance degradation degree.The proposed method was tested with the complete life data of a rolling bearing, and the result was compared with the analysis result by the traditional time-domain index feature extraction method.The superiority of the proposed method was verified.