Instantaneous frequency stability based SWT logistics equipment vibration signal noise reduction method

Abstract

The invention relates to an instantaneous frequency stability based SWT logistics equipment vibration signal noise reduction method in allusion to problems that the material transmission accuracy is reduced and the operation risk is increased because of failure problems such as deviation and abrasion of the traditional transmission equipment due to long-term frequency use. The method comprises thesteps of (1) collecting vibration signals by using a vibration acceleration sensor; (2) performing segmentation processing at intervals on the obtained vibration signals according to a time sequenceto obtain a sampling point set; (3) performing time-frequency decomposition on the vibration segment signals obtained in the step (2) by using synchronous extrusion wavelet transform to obtain a synchronous extrusion wavelet variable and time-frequency diagram information; (4) performing selective extraction and reduction on the variable obtained in the step (3); and (5) calculating an instantaneous frequency curve of an IMT component obtained in the step (4) by using Hilbert transform. The scheme of the invention is applied to the instantaneous frequency stability based SWT logistics equipment vibration signal noise reduction method.

Description

Technical field: [0001] The application of the present invention relates to the field of signal noise reduction, in particular to a method for noise reduction of vibration signals of SWT logistics equipment based on instantaneous frequency stability. Background technique: [0002] At present, with the development of the economy and the improvement of modernization, logistics gradually occupies an important position in society. Logistics machinery and equipment are the foundation and key to the development of logistics operations. Efficient and safe logistics equipment can improve the operational efficiency and technical level of logistics. Transmission equipment As an indispensable part of logistics machinery and equipment, due to long-term frequent use, there will be problems such as deviation and wear, which will reduce the accuracy of material transmission and increase operational risks; therefore, how to ensure its safe and reliable operation is the key to the safety and ...

AI Technical Summary

Problems solved by technology

[0002] At present, with the development of the economy and the improvement of modernization, logistics gradually occupies an important position in society. Logistics machinery and equipment are the foundation and key to the development of logistics operations. Efficient and safe logistics equipment can improve the operational efficiency and technical level of logistics. Transmission equipment As an indispensable part of logistics machinery and equipment, due to long-term frequent use, there will be problems such as deviation and wear, which will reduce the accuracy of material transmission and increase operational risks; therefore, how to ensure its safe and reliable operation is the key to the safety and efficiency of the entire logistics The key to the operation, at present, the transmission equipment fault diagnosis method based on vibration signal analysis has the characteristics of real-time and easy installation, and has been widely used in logistics machinery testing. However, the vibration signals collected in actual engineering applications contain a lot of noise. The effective information that can reflect the fault characteristics in the signal becomes very weak, so how to effectively suppress noise and improve the signal-to-noise ratio is the prerequisite and necessary link for fault detection based on vibration signals;

[0003] Aiming at the noise reduction problem of non-stationary signals, scholars at home and abroad have carried out a lot of in-depth research, which can be roughly divided into methods based on Fourier transform, wavelet transform and Ensemble Empirical Mode Decomposition (EEMD); these Each method has its own advantages and disadvantages. Among them, the Fourier transform method is mature in theory, but it is difficult to deal with the contradiction between noise suppression and edge information protection, and it is difficult to distinguish the frequency overlapping part; although the wavelet transform method has multi-resolution performance, its noise reduction effect Relying on wavelet basis and threshold selection, it will also be affected by the number of wavelet decomposition layers and sampling rate. Although the noise reduction method based on EEMD time-frequency analysis has a strong frequency band layer selection function and self-adaptive decomposition, this method is very easy. Affected by signal mutations, resulting in aliasing and breakpoint effects during decomposition

Even so, scholars have successively proposed many noise reduction methods based on EEMD decomposition. Among them, the traditional mandatory threshold method assumes that the noise mostly appears in the high frequency band, and reconstructs the signal by removing the first few intrinsic mode components (IMF) to achieve noise reduction. However, this method cannot deal with the situation where the noise appears in the low-frequency band. In addition, if the signal itself contains effective high-frequency information, this method will also lead to the loss of the original signal. Another scholar proposed to use each IMF component and the original signal The size of the mutual trust value is used to select the IMF component, but this method does not clearly explain how to determine the threshold; in addition, because the original signal itself contains noise components, this method may introduce false components to affect the noise reduction performance

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