Mechanical state monitoring method based on CELMDAN and SSKFDA

Abstract

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 the criterion of minimum intra-class local structure measurement and maximum full-local structure measurement. and (5) a mechanical known and unknown state detection methodbased on global monitoring statistics and Bayesian posteriori reasoning is proposed, and the problem that most mechanical monitoring systems cannot detect unknown abnormal states is well solved.

Description

technical field [0001] The invention relates to the technical field of mechanical equipment monitoring, in particular to a mechanical state monitoring method based on noise self-adaptive fully integrated local mean decomposition CELMDAN and semi-supervised coefficient kernel Fisher differential analysis SSKFDA. Background technique [0002] Rotating machinery is an important part of aircraft engines, gas turbines, reducers and other equipment. It is widely used in industries such as electric power, metallurgy, chemical industry and machinery. personal safety. Therefore, it is necessary to in-depth study of mechanical operating status and fault diagnosis, so as to reduce maintenance costs and ensure safe and stable production. Vibration signals contain rich state information of mechanical equipment components, and the method of state monitoring and fault diagnosis of rotating machinery based on vibration signals has been proved to be an effective and mainstream method. Howe...

AI Technical Summary

Problems solved by technology

It should be pointed out that ELMD still has the following problems: (1) ELMD decomposes the signal components and additive noise parts of the same scale into corresponding PFs, which requires multiple integration experiments to eliminate the additive noise part, which increases the calculation of the algorithm (2) The applied noise amplitude has a great influence on the performance of ELMD

Aiming at the problem that the sparse subspace learning method does not effectively use data to identify information, inspired by the idea of ​​LDA, Lei Yu (2017) proposed a sparse subspace learning method called sparse multiple maximum scatter difference (SMMSD)

Images