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In bearings defect diagnosis applications, information fusion has been widely used to improve identification accuracy for different types of faults, which may lead to high-dimensionality and information redundancy of the data and thus degenerate the classification performance. Therefore, it is a major challenge for machinery fault diagnosis to extract optimal features from high-dimensional and redundant data for classification. In addition, in order to guarantee the performance of fault diagnosis, conventional supervised methods usually
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