A Sparse Representation Method for Mechanical Faults Based on Wolves Parameterized Joint Dictionary

Abstract

The invention discloses a mechanical fault sparse representation method based on wolf group parameterized joint dictionary, which belongs to the technical field of mechanical fault diagnosis. The present invention combines the principle of sparse representation to propose a gray wolf optimized sparse method based on the joint dictionary, integrates the optimization algorithm into the orthogonal matching and tracking, selects the joint dictionary atom that best matches the fault through the inner product operation, and uses the optimal atom to process the fault signal Reconstruction, and then perform envelope analysis on the reconstructed signal, so as to realize the extraction of transient characteristic components in the signal, effectively characterize the fault information, and judge the fault type more accurately. The invention is suitable for fault diagnosis of rolling bearings.

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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