Abstract:  Aiming at following problems in the quantitative assessment of rolling bearing damage, such as the current feature extraction algorithms were prone to modal aliasing, slow convergence speed, and because of the bad robustness and the low accuracy of the evaluation index, the actual needs were difficult to be satisfied. An improved adaptive local interative filtering (AILF) algorithm was proposed as a performance degradation feature extraction algorithm. A quantitative evaluation algorithm of bearing damage based on the energy JRD distance between frequency bands was proposed. In order to improve the convergence speed and accuracy of AILF algorithm, the singular value decomposition (SVD) algorithm with principal component analysis (PCA) was used as the prefiltering unit of AILF algorithm. AILF was then used to adaptively iteratively decompose the preprocessed signals. Finally, taking the JRD distance of energy between frequency bands as the evaluation index, the quantitative evaluation experiment and accelerated life experiment of bearing damage state were carried out. The results of different damage states and accelerated life show that the proposed algorithm is effective in quantitatively evaluating bearing damage and monitoring the degradation of lifecycle performance. Comparing with the contrast algorithm, the proposed algorithm has better robustness and quantitative accumulation, and is more sensitive to identify the early performance degradation. The proposed algorithm has a good prospect in practical engineering applications.

Key words:  bearing; damage quantification assessment; performance degradation; adaptive local interative filtering(AILF)algorithm; JensenRényi divergence(JRD); singular value decomposition (SVD) algorithm