Gearbox compound fault diagnosis method based on VMD and OMEDA

Abstract

The invention relates to a gearbox compound fault diagnosis method based on VMD and OMEDA. The gearbox compound fault diagnosis method comprises the steps that firstly, a vibration acceleration signalof a fault gearbox is obtained, the penalty factor alpha in VMD is set to be 2000, thus the modal-aliasing angle is avoided, and the decomposing number K is determined; secondly, on the basis of determined VMD parameters, VMD decomposing is conducted on an original vibration signal, and all components are subjected to envelope demodulation analysis; then, whether protruding frequency lines and the double frequency exist in envelope spectrums of all the components or not is observed, if yes, the protruding frequency lines are compared with the fault character frequency of the gearbox, and if not, the components are subjected to OMEDA noise reduction continuously and then subjected to envelope demodulation analysis; and finally, the comparing results of the protruding spectrum lines of theenvelope demodulation spectrums of all the components and the fault character frequency are synthesized, and the compound fault type of the gearbox and the parts where faults are located are judged. Signal noise reduction and separation of all frequency bands are achieved through VMD, fault impact components covered by noise are further enhanced through OMEDA, and gearbox compound fault diagnosisis more accurate.

Description

technical field [0001] The invention relates to the field of fault diagnosis of rotating machinery, in particular to a compound fault diagnosis method of a gearbox based on VMD-OMEDA. Background technique [0002] As an indispensable part of the transmission system, the gearbox has been widely used in industry, transportation and military fields, and its operating status will directly affect the working condition of the entire equipment. Therefore, the health assessment and fault diagnosis of gearboxes have been receiving much attention. When the gearbox has a bearing-gear compound fault, due to the different mechanism of the fault and the different vibration signal path generated by the fault, the energy of each fault in the signal picked up by the sensor is also different, and the fault with small energy is naturally difficult to be detected. Coupled with the influence of strong background noise, it has been difficult to accurately diagnose compound faults. [0003] Sinc...

AI Technical Summary

Problems solved by technology

Although these methods have achieved certain results in the field of fault diagnosis of rotating machinery, there are still the following problems: (1) wavelet decomposition is difficult to achieve adaptive selection of wavelet bases and decomposition layers according to different signals; (2) EMD decomposition has endpoints Effect and modal aliasing phenomenon, EEMD decomposition Although the modal aliasing phenomenon in EMD is improved, the choice of white noise is not adaptive

Under strong background noise, it is difficult to completely extract all fault information only by using VMD decomposition, so further noise reduction processing is required for unsatisfactory signals after decomposition

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