Robust bearing performance degradation assessment method based on improved wavelet packet–support vector data description

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.