Mechanical fault diagnosis method based on parameter adaptive VMD

A technology for mechanical faults and diagnosis methods, applied in the testing of mechanical components, genetic models, genetic laws, etc., can solve problems such as information omission, influence of decomposition results, and difficulty in obtaining satisfactory analysis results, and achieve strong signal detection capabilities and accurate analysis. the effect of the result

Active Publication Date: 2018-10-23
SICHUAN UNIV
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Problems solved by technology

However, the decomposition parameters of the VMD method (the number of modal components and the frequency bandwidth control parameters of the modal components) have a significant impact on its decomposition results
At present, in the field of mechanical failure, the VMD decomposition parameters are specified in advance in most studies, and it is difficult to obtain satisfactory analysi

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  • Mechanical fault diagnosis method based on parameter adaptive VMD
  • Mechanical fault diagnosis method based on parameter adaptive VMD
  • Mechanical fault diagnosis method based on parameter adaptive VMD

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[0052] The present invention uses the weighted kurtosis index (KCI) as the fitness function, adaptively obtains the VMD decomposition parameter that best matches the signal to be analyzed through the optimization algorithm, and uses the best matching VMD decomposition parameter to perform VMD decomposition on the original vibration signal, and then Realize mechanical fault feature extraction and fault diagnosis. Since the weighted kurtosis index comprehensively considers the characteristics of the modal components and the correlation between the modal components and the original signal, using it as the optimization objective function can effectively avoid the problem of information omission, thereby improving the signal detection ability of the VMD method in mechanical fault diagnosis. , to achieve accurate diagnosis of mechanical faults.

[0053] The embodiment provides a method for diagnosing mechanical faults, and the workflow is as follows: figure 1 shown, the specific st...

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Abstract

The invention relates to the field of mechanical vibration signal processing and fault diagnosis, and discloses a mechanical fault diagnosis method based on a parameter adaptive VMD. The signal detection capability of the VMD method in fault diagnosis is improved, and accurate mechanical fault diagnosis is realized. According to the invention, a weighted kurtosis index is used as a fitness function; a VMD decomposition parameter which best matches a signal to be analyzed is acquired through the self-adaption of an optimization algorithm; the optimal VMD decomposition parameter is used to carryout VMD decomposition on an original vibration signal; and mechanical fault feature extraction and fault diagnosis are realized. The method provided by the invention is suitable for mechanical faultdiagnosis.

Description

technical field [0001] The invention relates to the field of mechanical vibration signal processing and fault diagnosis, in particular to a mechanical fault diagnosis method based on parameter adaptive VMD. Background technique [0002] Once key mechanical equipment fails, it will bring huge economic losses and even casualties. Major accidents caused by mechanical equipment failures have occurred frequently at home and abroad. If the fault can be found in time during the operation of the equipment, it is of great significance to the safe operation of the equipment and the avoidance of economic losses and catastrophic accidents. [0003] An arduous task of mechanical fault diagnosis is how to extract useful fault feature information from complex monitoring signals, which has attracted extensive attention in the industry and academia in recent years. The variational mode decomposition (VMD) method is a signal adaptive decomposition method proposed by UCLA scholars Dragomirets...

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Application Information

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IPC IPC(8): G01M13/02G01M13/00G06F17/11G06N3/00G06N3/12
CPCG01M13/00G01M13/021G01M13/028G06F17/11G06N3/006G06N3/126
Inventor 苗强张新刘慧宇曾小飞莫贞凌王剑宇王磊张恒
Owner SICHUAN UNIV
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