| Summary: | There are two difficulties in the remaining useful life prediction of rolling bearings. First, the vibration signals are always interfered by noise signals. Second, some of the extracted features include useless information which may decrease the prediction accuracy. In order to solve the problems above, corresponding methods are employed in this article. First, adaptive sparsest narrow-band decomposition is utilized for extracting the degradation information from noise. Compared with the commonly used empirical mode decomposition method, problems including mode mixture and boundary effect caused by the calculation of extremas is not required. Second, locality-preserving projection is applied for merging the meaningful information from the original data and reduces the dimension of features. Based on adaptive sparsest narrow-band decomposition and locality preserving projection, a novel approach is employed for the remaining useful life prediction. The prediction procedure is as follows. First, the signals are analyzed by adaptive sparsest narrow-band decomposition and the feature vectors are constructed. Afterwards, the features are fused by locality preserving projection to merge useful information from the features. Least squares support vector machine is applied for the remaining useful life prediction in the end. The analysis results indicate that the proposed approach is reliable for rolling bearing remaining useful life prediction.
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