Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
565679 | Mechanical Systems and Signal Processing | 2009 | 13 Pages |
Bearing performance degradation assessment is one of the most important techniques in proactive maintenance aiming to realize equipment's near-zero downtime and maximum productivity. In this paper, we propose a new robust method for it based on improved wavelet packet decomposition (IWPD) and support vector data description (SVDD). A health index is designed based on general distance. Node energies of IWPD are used to compose feature vectors. Based on feature vectors extracted from normal signals, a SVDD model fitting a tight hypersphere around them is trained, the general distance of test data to this hypersphere is used as the health index. Research results of its application in a bearing accelerated life test show that this health index can reflect effectively bearing performance degradation comparing with many other parameters.