Mechanical fault sparse representation method based on wolf pack parameterization joint dictionary

Abstract

The invention discloses a mechanical fault sparse representation method based on a wolf pack parameterization joint dictionary, and belongs to the technical field of mechanical fault diagnosis. The invention provides a grey wolf optimization sparse method based on a joint dictionary in combination with a sparse representation principle. An optimization algorithm is fused into orthogonal matching pursuit, a joint dictionary atom most matched with a fault is selected through inner product operation, the optimal atom is used for reconstructing a fault signal, and then envelope analysis is performed on the reconstructed signal, so that extraction of transient characteristic components in the signal is realized, fault information is effectively represented, and the fault type is judged more accurately. The method is suitable for rolling bearing fault diagnosis.

Description

technical field [0001] The invention belongs to the technical field of mechanical fault diagnosis, and in particular relates to a sparse representation method of mechanical faults based on a wolf pack parameterized joint dictionary. Background technique [0002] In recent years, after the hoisting and transportation machinery manufacturing industry adopts new theories, new technologies, new structures, new materials and new processes, its product design and performance levels have been improved to a certain extent. As an important part of mechanical system components, rolling bearings often fail during the operation of the mechanical system, and once a failure occurs, it will have a great impact on the normal operation of the equipment. Therefore, it is particularly important to monitor the condition of the machine and diagnose early faults , so as to ensure the safe operation of the equipment in the later stage. [0003] Different from the traditional signal representation...

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Problems solved by technology

[0004] At present, the Gabor atomic orthogonal matching pursuit (OMP) method based on the genetic algorithm reconstructs the signal, and the extraction of mechanical fault features and the accurate identification of fault types are poor, and it is increasingly difficult to meet today's production requirements.

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