In state analysis of rolling bearings using collaborative representation theory, how to construct an excellent redundant dictionary\nto collaboratively represent the acquired normal or abnormal data has been being a significant issue. Thus, a new method for fault\ndetection and classification of rolling bearings is proposed in this paper. The proposed algorithm mainly consists of three\ncomponents. First, a wavelet transform is employed to extract features, which takes advantage of the observation that vibration\nsignals under different conditions have similar frequency spectra. This similarity ensures that we can collaboratively represent any\ntest sample by using training samples. Second, under the similarity assumption, a dictionary pair learning strategy is employed to\nbuild an overcomplete dictionary pair, which is used to realize an optimal representation of the vibration signal. Meanwhile, the\nsparse constraint is also taken into account during dictionary training to enhance the robustness of the classification. Finally, the\nlearned dictionary combined with collaborative representation is used to intelligently perform pattern classification of rolling\nbearings. The effectiveness and superiority of the method are verified by applying the proposed algorithm on the simulated and\nreal vibration signals. The results show that, for different fault categories generated from different fault size and motor loads, our\nmethod can rapidly and accurately identify the fault category to which the input sample belongs.
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