Mechanical state monitoring method based on CELMDAN and SSKFDA

The invention discloses a CELMDAN and SSKFDA-based mechanical state monitoring method. The CELMDAN and SSKFDA-based mechanical state monitoring method includes the following steps that: (1) a CELMDANmethod is adopted to decompose complex vibration signals into a plurality of product functions with physical significance; (2) a method of taking a periodic modulation intensity PMI as a PFs selectioncriterion is provided, so that effective PFs can be accurately selected. (3) an SSKFDA dimension reduction method is provided, geometrical information of a label sample and an unlabeled sample set isfully utilized, a kernel method, sparse representation, manifold learning and an FDA method are fused, a low-dimensional subspace data set embedded in a high-dimensional sparse space is better disclosed, and the problem of dimension reduction of high-dimensional, sparse and nonlinear data is solved. (4) a rapid SSKFDA model selection method is proposed, according to the method, optimal model parameters are solved based on