Abstract
Compared with a single fault, the occurrence, evolution, and composition of coupling faults have more uncertainties and diversities, which make coupling fault classification a challenging topic in academic research and industrial application areas. This brief addresses the classification problems of coupling faults from a new perspective. Specifically, the main innovations are: 1) a classification framework for coupling faults is first proposed, which integrates multiple kernel learning and multilabel dimensionality reduction; 2) label correlations and nonlinear characteristics among coupling faults are fully explored aiming at improving classification performance; and 3) a trace ratio form of l_1 norm-based objective function is designed for improving the robustness of multilabel classifier. Extensive experiments on the hot rolling process (HRP) are finally given to validate the effectiveness of the proposed scheme.
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