Intelligent gear defect analysis method based on fractional wavelet transform and BP neutral network

A BP neural network and wavelet transform technology, which is applied in the testing of machine gears/transmission mechanisms, etc., can solve problems such as the inability to characterize the local characteristics of the signal, and the inability to simultaneously achieve the resolution of the instant domain and the fractional domain.

Active Publication Date: 2015-07-22
BEIJING UNIV OF TECH
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Problems solved by technology

Although the fractional Fourier transform has many unique properties, it cannot characterize the local characteristics of the signal; the short-time fractional Fourier transform has the defect of resolut

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  • Intelligent gear defect analysis method based on fractional wavelet transform and BP neutral network
  • Intelligent gear defect analysis method based on fractional wavelet transform and BP neutral network
  • Intelligent gear defect analysis method based on fractional wavelet transform and BP neutral network

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Embodiment Construction

[0051] The present invention will be further described in detail below in conjunction with the accompanying drawings, so that those skilled in the art can implement it with reference to the description.

[0052] In the experiment, the attached figure 1 The experimental bench shown is used for gear vibration signal acquisition during the speed-up phase, because the gear vibration signal during the speed-up phase is similar to a chirp signal, which is conducive to fractional wavelet denoising, and the vibration signal is collected using an acceleration sensor. attached figure 2 It is the flowchart of intelligent analysis of gear defects. The time-domain and frequency-domain diagrams of normal meshing gears, gears with large meshing gaps and gears with small meshing gaps are shown in the attachment Figure 3a-3f As shown, the illustration shows that the three different modes of gears cannot be distinguished by observing the vibration signal waveform or spectral line, so the an...

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Abstract

The invention discloses an intelligent gear defect analysis method based on fractional wavelet transform and a BP neutral network. The intelligent gear defect analysis method includes: taking transform order as a variable to perform fractional Fourier transform on gear vibration signals to determine optimal order, and performing fractional wavelet transform on the gear vibration signals under the optimal order for denoising to realize separation of useful component and background noise of the gear vibration signals; calculating feature parameters of the signals after being denoised to form a group of feature vectors which are used for representing features of gear vibration after denoising; averagely dividing the feature vectors into two groups which serve as a training sample and a testing sample respectively, and inputting the feature vectors into the BP neutral network for learning and classifying. By the intelligent gear defect analysis method, background noise mixed in the gear meshing vibration signals is inhibited well, useful signal component related to defects is retained, and gear defect features can be extracted effectively; self learning and classifying capability of the BP neutral network is utilized, so that defect mode of gears can be quickly recognized qualitatively with high accuracy.

Description

technical field [0001] The invention relates to an intelligent analysis method for gear defects, in particular to an intelligent analysis method for gear defects based on Fractional Wavelet Transform (FRWT) and BP (Back Propagation) neural network, belonging to the field of gear matching detection and fault diagnosis. Background technique [0002] As an indispensable mechanical component for transmitting power and motion in modern mechanical structures, gears have the advantages of large load-carrying capacity, high transmission precision, and constant transmission power, and are widely used in mechanical equipment. With the development of large-scale, complex, automatic and continuous equipment, the loss of the entire production caused by the defects and failures of the gears will become greater and greater. Therefore, quality testing must be carried out in all aspects of gear production and operation. Especially before the gears are installed, the gears must be paired and...

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Application Information

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IPC IPC(8): G01M13/02
Inventor 陈洪芳赵允石照耀
Owner BEIJING UNIV OF TECH
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