Bearing fault classification diagnosis method based on sparse representation and LDM (large margin distribution machine)

The invention provides a bearing fault classification diagnosis method based on sparse representation and an LDM (large margin distribution machine), overcomes the defects that signal decomposition is incomplete, a reconstructed signal cannot better keep features of an observed signal and the like in the conventional single-channel mechanical compound fault diagnosis method. According to the method, signal conversion from one dimension to high dimension is realized with a CEEMD (complete ensemble empirical mode decomposition) method, the decomposition completeness is guaranteed, and a mode mixing phenomenon is inhibited; meanwhile, a dimensionality reduction method based on sparse representation is introduced into a feature extracting and processing process of a blind source signal, data are subjected to sparse reconstruction through sparse representation, and data feature information is extracted from global data, so that the reconstructed signal can better keep the data features of the observed signal; further, the LDM classification method is introduced into a model fault type classification processing process of a to-be-detected bearing, and the accuracy and effectiveness of bearing fault diagnosis can be improved by aid of the generalization ability of the LDM classification method.